Multiagent optimization system Research Papers

2025

The traveling salesman problem, or TSP for short, is easy to state: given a nite number of cities" along with the cost of travel between each pair of them, nd the cheapest way of visiting all the cities and returning to your starting... more

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The backbone of the travelling salesperson

by John Slaney

2025

We study the backbone of the travelling salesperson optimization problem. We prove that it is intractable to approximate the backbone with any performance guarantee, assuming that P =NP and there is a limit on the number of edges falsely... more

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Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers

by Gabriela Ochoa

2024, Evolutionary Computation in Combinatorial Optimization

Understanding why some problems are better solved by one algorithm rather than another is still an open problem, and the symmetric Travelling Salesperson Problem (TSP) is no exception. We apply three state-of-the-art heuristic solvers to... more

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Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers

by Gabriela Ochoa

2024, Evolutionary Computation in Combinatorial Optimization

Understanding why some problems are better solved by one algorithm rather than another is still an open problem, and the symmetric Travelling Salesperson Problem (TSP) is no exception. We apply three state-of-the-art heuristic solvers to... more

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Heterogeneous Selection Genetic Algorithms For Traveling Salesman Problems

by Cheng-yan Kao

2024, Engineering Optimization

The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses,... more

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A Distributed Hybrid Algorithm for the Graph Coloring Problem

by Khaled Ghedira

2024, Lecture Notes in Computer Science

We propose a multi-agent based Distributed Hybrid algorithm for the Graph Coloring Problem (DH-GCP). DH-GCP applies a tabu search procedure with two different neighborhood structures for its intensification. To diversify the search into... more

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Naive and heuristic permutation-coded genetic algorithms for the quadratic knapsack problem

by Bryant Julstrom

2024, Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

The Quadratic Knapsack Problem extends the 0-1 Knapsack Problem by associating values not only with individual objects but also with pairs of objects. Two genetic algorithms encode candidate solutions to this problem as permutations of... more

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Evolving instances of unconstrained binary quadratic programming that challenge a tabu search heuristic

by Bryant Julstrom

2024, Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

A Tabu Search heuristic for unconstrained binary quadratic programming performs perfectly on a range of random problem instances. A genetic algorithm searches spaces of UBQP instances for instances that challenge the heuristic. The GA's... more

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Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem

by Bryant Julstrom

2024, Proceedings of the 7th annual conference on Genetic and evolutionary computation

Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem extends the familiar Knapsack Problem by assigning values not... more

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Further results of Gravitational Swarm Intelligence for Graph Coloring

by Israel López Ruiz

2024, 2011 Third World Congress on Nature and Biologically Inspired Computing

We provide enhanced results of an innovative nature inspired algorithm to solve the Graph Coloring Problem (GCP): the Gravitational Swarm Intelligence (GSI). Swarm Intelligence solves complex problems by extracting information from the... more

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Parallel noising methods embedded in an adaptive memory

by Theo Crainic

2023

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Parallel noising methods embedded in an adaptive memory

by Michel Toulouse

2023

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Improving EAX with restricted 2-opt

by Sheng-An Lee

2023, Proceedings of the 7th annual conference on Genetic and evolutionary computation

Edge Assembly Crossover (EAX) is by far the most successful crossover operator in solving the traveling salesman problem (TSP) with Genetic Algorithms (GAs). Various improvements have been proposed for EAX in GA. However, some of the... more

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Generalized quadratic multiple knapsack problem and two solution approaches

by Tuğba Sarac

2023, Computers & Operations Research

The Quadratic Knapsack Problem (QKP) is one of the well-known combinatorial optimization problems. If more than one knapsack exists, then the problem is called a Quadratic Multiple Knapsack Problem (QMKP). Recently, knapsack problems with... more

Fig. 1. Schematic display of an injection machine

12 Factors affecting the structure of test problems and levels of these factors.

Parameter Levels Table According to the 1/128 Fractional Factorial Experiments.

Computational Results for Generated Test Instances with n=30.

Number of the best results of test instances with n=30 for different paramete: levels.

Computational Results for Generated Test Instances with n=300. Table 7

Number of the Best Results of Test Instances with n=300 for Different Paramete Levels. most of the test instances. This means there is a tradeoff between the solution time and the objective function value. If the solution time is not so important, a decision maker should choose the hybrid algorithm to solve this problem. Table 8 summarizes the results in Table 7.

Test results of the instances with 10 items and 2 knapsacks. Tablo 9 was developed and coded using MS Excel VBA. All of these instances were solved using enumeration, GAMS/Dicopt, GA and HA. The obtained results are given in Table 9.

* A maximum of 5 iterations limit was set for HA. Test results for the real-life problem with n=500.

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Finding critical backbone structures with genetic algorithms

by Adam Prugel-Bennett

2023, Proceedings of the 9th annual conference on Genetic and evolutionary computation

This paper introduces the concept of a critical backbone as a minimal set of variables or part of the solution necessary to be within the basin of attraction of the global optimum. The concept is illustrated with a new class of test... more

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Impact of grafting a 2-opt algorithm based local searcher into the genetic algorithm

by Tuba Milan

2023, Proceedings of the 9th WSEAS …

Abstract:-This paper examines the impact of grafting a 2-opt based local searcher into the standard genetic algorithm for solving the Travelling Salesman Problem with Euclidean distance. Pure genetic algorithms are known to be rather... more

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A Novel Exact Heuristic Graph Coloring Algorithm based on Finding Independent Set

by Sukrati Agrawal

2023, International Journal of Computer Applications

Vertex coloring is a graph coloring technique which has a wide application area to provide solution for many real world problems. The high computational complexity of graph coloring algorithm led the development of exact heuristic... more

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A hybrid approach of GA and ACO for TSP

by Xiaogang Ruan

2023, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788)

This paper proposed a hybrid approach of Genetic Algorithm (CA) and Ant Colony Optimization (ACO) for the Traveling Salesman Problem. In this approach, every chromosome of GA is at the same time an ant of ACO.' Whenever GA performs the... more

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A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover

by Hiếu Hà

2023, Parallel Problem Solving from Nature, PPSN XI

We present a hybrid Genetic Algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for the Traveling Salesman Problem (TSP). GPX is respectful,... more

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The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt

by Charlie Ross

2023, Parallel Problem Solving from Nature

Optimal results for the Traveling Salesrep Problem have been reported on problems with up to 3038 cities us- ing a GA with Edge Assembly Crossover(EAX). This paper first attempts to independently replicate these results on Padberg's... more

Table 2. Number of evaluations required for convergence.

Table 3. A comparison of the best algorithms with 2-opt using partial restarts.

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A cooperative group optimization system

by Zunjing Wang

2023, Soft Computing

A cooperative group optimization (CGO) system is presented to implement CGO cases by integrating the advantages of the cooperative group and lowlevel algorithm portfolio design. Following the natureinspired paradigm of a cooperative... more

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A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover

by Hiếu Hà

2023, Parallel Problem Solving from Nature, PPSN XI

We present a hybrid Genetic Algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for the Traveling Salesman Problem (TSP). GPX is respectful,... more

Fig. 1. An example of (a) The graph G created from the union of two parent tours with two partitions shown by the heavy dark lines and (b) The graph G,, constructed by deleting the common edges between the two parent tours from G

Table 2. Average percentage of the cost of the minimum tour found above the globally optimal cost averaged over 500 experiments using Chained LK and the Hybrid GA.

Table 4. Results obtained by running the hybrid GA for only 5 generations and without mutation (double-bridge moves). “Global Edges in population” is the number of edges found in the global optimum that are also present in the population. “Global Edges in Minimal Tour” is the number of edges shared in common between the best solution found and the global optimum. “Unique Edges in Population” is the total number of distinct edges found in the population at the end of generation 5. The number of edges in each instance is indicated by the numerical suffix of the instance name Table 3. The number of times the global optimum is found by each algorithm after 1010 calls to LK-search over 50 experiments.

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A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover

by Minh Hiếu Hà

2023, Parallel Problem Solving from Nature, PPSN XI

We present a hybrid Genetic Algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for the Traveling Salesman Problem (TSP). GPX is respectful,... more

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The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt

by Jason Denton

2023, Parallel Problem Solving from Nature

Optimal results for the Traveling Salesrep Problem have been reported on problems with up to 3038 cities us- ing a GA with Edge Assembly Crossover(EAX). This paper first attempts to independently replicate these results on Padberg's... more

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A Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem

by Jiongzhi Zheng

2022, SSRN Electronic Journal

We propose a powerful Reinforced Hybrid Genetic Algorithm (RHGA) for the famous NP-hard Traveling Salesman Problem (TSP). RHGA combines reinforcement learning technique with the well-known Edge Assembly Crossover genetic algorithm... more

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Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem

by Jiongzhi Zheng

2022, Proceedings of the AAAI Conference on Artificial Intelligence

We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods... more

Figure 1: The choice of x;, y;, xj+1 and y;+ in k-opt.

Figure 2: Q-value evaluation on cumulative gap and runtime (instances are sorted in ascending order of problem size).

Figure 3: Composite versus single reinforcement methods on cumulative gap and runtime.

Table 1. Comparison of VSR-LKH and DRL algorithms on five easy instances (best results in bold).

Table 2. Comparison of VSR-LKH and LKH on some hard instances, best results in bold (see full results in Appendix).

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A Parallel and Concurrent Implementation of Lin-Kernighan Heuristic (LKH-2) for Solving Traveling Salesman Problem for Multi-Core Processors using SPC3 Programming Model

by Muhammad Ali Ismail

2022, International Journal of Advanced Computer Science and Applications

With the arrival of multi-cores, every processor has now built-in parallel computational power and that can be fully utilized only if the program in execution is written accordingly. This study is a part of an ongoing research for... more

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A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling between Local Optima

by Gabriela Ochoa

2022, Evolutionary Computation

Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of [Formula: see text] reachable offspring, where [Formula: see... more

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Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers

by Jason Adair

2022, Evolutionary Computation in Combinatorial Optimization

Understanding why some problems are better solved by one algorithm rather than another is still an open problem, and the symmetric Travelling Salesperson Problem (TSP) is no exception. We apply three state-of-the-art heuristic solvers to... more

Fig. 1: Instance type problem domains. Plots (a)—(c) are examples of the RUE, MORPH & NETGEN instances, all with n = 500. The MORPH example, (b), is constructed from (a) and (c), with a = 0.5. Plot (d) shows rat575, a rattled grid of 575 locations; plot (e) is the World instance with 734 town/cities in Uruguay (uy734); and (f) is the VLSI problem denoted as dkg813

Fig.2: EAX v LKH-+IPT runtime performance results. All plot axes are log- scaled; scatter points are median values of 30 runs of pairwise instances in the form (median(EAX), median(LKH+IPT)); diagonal lines represent equality of median performance; boxplots on axes show distribution of median runtime val- ues Firstly, the relative performances of EAX versus 107.7s. Similar values are returned for the MORP runtime = 7.6s and median LKH+IPT = 116.4s. LKH-+IPT are considered, and results are shown in Fig. 2 and Table 1. It can be seen from Fig. 2(a) and (b) that EAX is vastly superior in solving NETGEN and MORPH instance types to optimality when compared with LKH+IPT. The median value of the boxplot or EAX in Fig. 2(a) (which is the overall median value of all EAX runs for ETGEN) is 7.2 seconds (s), whereas the corresponding value for LKH+IPT is H instances, with median EAX

Fig. 3: LKH+GPX2 v EAX runtime performance results. All plot axes are log-scaled; scatter points are median values of 30 runs of pairwise instances (LKH+GPX2, EAX); diagonal lines represent equality of median performance; boxplots on axes show distribution of median runtime values

Fig. 4: LKH+IPT v LKH+GPX2 runtime performance scatter plots As inferred by their relative performances against EAX, the runtime results for LKH+IPT versus LKH+GPX2 exhibit very little variation. Both Fig. 4 and Table 3 show that, for instances of these sizes and types, and with a stopping criterion of one hour, there exists little appreciable difference in their relative performances. The four plots in Fig. 4 show that almost all scatter points lie very closely to the line of median equality. When this did not hold, for example in Fig. 4(d) where a single point is located at (1329, PAR10) (corresponding to VLSI instance bck2217), the Mann-Whitney test does not always allow rejection of its null hypothesis and so the instance is labelled as ANY.

It is important to be able to identify instance features which can inform specific algorithm selection. To this end, a decision tree model was trained using the Pihera and TSPMETA feature sets described earlier. A decision tree is a simple but useful technique for creating “rules of thumb ” that can be applied to identify instance traits that may hamper the performance of specific solvers. This process identified key features (and their associated values) which can be used to select the most effective instance-specific algorithms. From the head of the decision tree shown in Fig. 5, two key features were extracted which can be used to identify a significant variance in performance between the EAX and SET-LKH solvers: nn5.sc.max.n, from the Pihera feature set; and mst_dists_median, from the TSPMETA feature set. It can be seen that 72% of instances were solved significantly faster by EAX than either of the SET-LKH heuristics and, in a further 16%, no significan difference was found (cf. Table 5 & Fig. 5(a)). The intuitive choice, therefore would be to use EAX by default; however, note that 12% of all instances testec were best solved by the SET-LKH heuristics. It was found that 67% of all in stances exhibited nn5.sc.max.n < 0.71; of these, 93% were solved best by EAX (cf. Figures 5(b) and 6(a)). When nn5.sc.max.n >= 0.71, 71% were solved by SET-LKH heuristics in comparable (29%) or less (42%) time, Fig. 5(c).

Fig. 6: Plots show kernels of ANY, EAX, SET-LKH classed instances for features nn5.sc.max.n (a) and mst_dists_median (b). Vertical lines indicate the threshold values obtained from the decision tree in Fig. 5

For the LIB instances, EAX still performed better than LKH+IPT; how- ever, EAX did not overwhelmingly outperform LKH+IPT as it had for the NETGEN and MORPH instance types. The 3 World instances included in out study (uy734, zi929 and mu1979) were solved significantly better by EAX than LKH-+IPT. Focusing on VLSI only, the median runtime value for EAX was 12.8s, with LKH+IPT returning a median runtime of 59.2s. When considering these with the results in Table 1, it is apparent that EAX solves VLSI instances tc optimality with noticeably greater speed than LKH+IPT.

Excluding the RUE instances, the proportion best solved by the classifica- tions EAX or ANY rises to 96.6%. Conversely, considering only the RUE in- stances, we see that the SET-LKH heuristics perform effectively 72.5% of the time when combined with the ANY classification. The VLSI instances, often con- sidered the most intractable or difficult to solve to optimality, are best solved by EAX 68.4%; only 14.0% are solved significantly quicker by SET-LKH solvers.

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A Novel Exact Heuristic Graph Coloring Algorithm based on Finding Independent Set

by Sukrati Agrawal

2022, International Journal of Computer Applications

Vertex coloring is a graph coloring technique which has a wide application area to provide solution for many real world problems. The high computational complexity of graph coloring algorithm led the development of exact heuristic... more

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An ant-based algorithm for coloring graphs

by Kimanh Phan

2022, Discrete Applied Mathematics

This paper presents an ant-based algorithm for the graph coloring problem. An important difference that distinguishes this algorithm from previous ant algorithms is the manner in which ants are used in the algorithm. Unlike previous ant... more

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Pareto-optimal Algorithms for Metric TSP: Experimental Research

by Sergey Avdoshin

2022, International Journal of Open Information Technologies

The Travelling Salesman Problem (TSP) is a fundamental task in combinatorial optimization. A special case of the TSP is Metric TSP, where the triangle inequality holds. Solutions of the TSP are generally used for costs minimization, such... more

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The Metric Travelling Salesman Problem: Pareto-optimal Heuristic Algorithms

by Sergey Avdoshin

2022

The Metric Travelling Salesman Problem is a subcase of the Travelling Salesman Problem (TSP), where the triangle inequality holds. It is a key problem in combinatorial optimization. Solutions of the Metric TSP are generally used for costs... more

Fig. 1. Pareto-optimal algorithms for XMC10150.tsp (WV = 10150) and KZ9976.tsp (N = 9976). The charts for pair with N ~ 10000 are shown below (see 1). The name of TSPLIB instance is shown in chart title. The horizontal axis represents the time per- formance of methods in seconds. The vertical axis shows the gap between optimal and obtained solutions, expressed in percent. Pareto-optimal methods are highlighted in red. The points which are represented by Pareto solutions are bigger than non- Pareto-optimal solutions.

Table 1. Upper-bound estimates of algorithms Table 2. Running time of algorithms

Table 3. Pairs of input datasets from VLSI and National TSPs

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The Metric Travelling Salesman Problem: The Experiment on Pareto-optimal Algorithms

by Sergey Avdoshin

2022, Proceedings of the Institute for System Programming of the RAS

The Metric Travelling Salesman Problem is a subcase of the Travelling Salesman Problem (TSP), where the triangle inequality holds. It is a key problem in combinatorial optimization. Solutions of the Metric TSP are generally used for costs... more

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A Combined Procedure Based on Gaussian Process Regression for the Traveling Salesman Problem

by Juta Pichitlamken

2022

This study aims to propose a new method of estimating the optimal tour given the arbitrary sample tours. In this study, a probabilistic approach, namely Gaussian process regression (GPR), is employed to solve the symmetric traveling... more

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A Multiagent Approach for Metaheuristics Hybridization applied to the Traveling Salesman Problem

by Elizabeth F G Goldbarg

2022

Abstract-This paper proposes a multiagent approach for metaheuristics hybridization inspired on the popular technique called Particle Swarm Optimization (PSO). In the proposed approach, agents develop a society with collaboration to... more

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Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm

by Sana Salous

2022, Journal of Computer and Communications

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full... more

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The 0-1 Quadratic Knapsack Problem

by Dalomi Bahan

2022

The quadratic knapsack problem (QKP) is the problem of maximizing a quadratic objective function subject to a single knapsack constraint. It is a well-known NP-hard combinatorial problem. In this paper, we examine the use of Alienor... more

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A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling Between Local Optima

by Gabriela Ochoa

2022, Evolutionary Computation

Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of [Formula: see text] reachable offspring, where [Formula: see... more

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Linear Linkage Encoding in Grouping Problems: Applications on Graph Coloring and Timetabling

by emre korkmaz

2022, Practice and Theory of Automated Timetabling

Linear Linkage Encoding (LLE) is a recently proposed representation scheme for evolutionary algorithms. This representation has been used only in data clustering. However, it is also suitable for grouping p roblems. In this paper, we... more

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A Genetic Algorithm for the Quadratic Multiple Knapsack Problem

by Tugba Saraç

2022

The Quadratic Multiple Knapsack Problem (QMKP) is a generaliz- ation of the quadratic knapsack problem, which is one of the well-known combinatorial optimization problems, from a single knapsack to k knapsacks with (possibly) different... more

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A New Ant Algorithm for Graph Coloring

by Alain Hertz

2022

Let G = (V;E) be a graph with vertex set V and edge set E. The k-coloring problem is to assign a color (a number chosen in {1, ..., k}) to each vertex of V so that no edge has both endpoints with the same color. We describe in this paper... more

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A Genetic Algorithm for the Quadratic Multiple Knapsack Problem

by Tugba Saraç

2021, Lecture Notes in Computer Science

The Quadratic Multiple Knapsack Problem (QMKP) is a generalization of the quadratic knapsack problem, which is one of the well-known combinatorial optimization problems, from a single knapsack to k knapsacks with (possibly) different... more

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New Order-Based Crossovers for the Graph Coloring Problem

by Christine Mumford

2021, Lecture Notes in Computer Science

Huge color class redundancy makes the graph coloring problem (GCP) very challenging for genetic algorithms (GAs), and designing effective crossover operators is notoriously difficult. Thus, despite the predominance of population based... more

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New Order-Based Crossovers for the Graph Coloring Problem

by Christine Mumford

2021, Parallel Problem Solving from Nature

Huge color class redundancy makes the graph coloring problem (GCP) very challenging for genetic algorithms (GAs), and designing effective crossover operators is notoriously difficult. Thus, despite the predominance of population based... more

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Modified Local Search Heuristics for the Symmetric Traveling Salesman Problem

by Alfonsas Misevičius

2021, Information Technology And Control

In this paper, we investigate some modified local search (LS) heuristics for the solution of symmetric traveling salesman problem (TSP). These modifications are mainly due to the use of extended neighborhood structures. In addition, we... more

this type of moves. Note that the 2-exchange move is symmetric as p’ = p | move(i, j) > p=p' Li move(i, j). Figure 1. Illustrative example of the 2-edge-exchange move

Figure 4. Graphical representation of the neighborhoods @, and @,,, the number of the sub-neighborhoods covered remains constant, the time complexity of the neighborhood exploration is proportional to O(n’), where n is the problem size. In the case of the fast local search, the complexity reduces to O(Kn), where K is the candidate list size.

Figure 8. Graphical representation of the neighborhood 0,,,..: Figure 7. Graphical representation of the neighborhood @,,,.,

Figure 6. Graphical representation of the neighborhood 0,,,6.6

Figure 5. Graphical representation of the neighborhood 0,,,.,

Figure 10. Illustrative example of the 3-edge-exchange move

Figure 11. Illustrative examples of the different possible variants of 4-edge-exchange moves

Figure 12. Illustrative examples of the different possible variants of 5-edge-exchange moves

Figure 14. Time-to-target plots for the TSPLIB instance gr96 particular, the performance improvement factors PIF, here, fo. denotes the time needed to obtain the given target value with the probability 0.5. In Table 3, we present the approximate values of the performance improvement factors for the algorithms LS«@,), FLS(@,), IFLS(@2), and IEFLS( ©5926. )) In narticnlar the nerfarmance imnravement factare P/F,

* the numeral in the instance name indicates the size of the problem, i.e. the number of cities. Table 1. Results of the experiments with the local search heuristics on TSPLIB instances

Table 2. Results of the comparison of different heuristic algorithms on TSPLIB instances corresponding optimal solution values for n < 300 and n > 300, respectively). From Table 3, it could be seen that the performance improvement factors of IEFLS to LS, IEFLS to FLS, and IEFLS to IFLS, averaged over 30 instances, are roughly equal to 6.6, 4.9, and 2.7,

Table 3. Performance improvement factors for the local search heuristics on TSPLIB instances run time performance point of view. It has also been learned that, by integrating these modifications into the iterated local search framework, the performance is increased even more significantly. One more interesting observation is that the "breadth-first" like search appears to be preferable to the "depth-first" strategy at least for the TSP instances examined. 5. Concluding remarks

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New Order-Based Crossovers for the Graph Coloring Problem

by Christine Mumford

2021

Huge color class redundancy makes the graph coloring problem (GCP) very challenging for genetic algorithms (GAs), and designing effective crossover operators is notoriously difficult. Thus, despite the predominance of population based... more

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Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm

by Iztok Fister

2021, Computational Optimization and Applications

This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor... more

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Quantum Inspired Particle Swarm Combined with Lin-Kernighan-Helsgaun Method to the Traveling Salesman Problem

by Maria Teresinha Arns Steiner

2021, Pesquisa Operacional

The Traveling Salesman Problem (TSP) is one of the most well-known and studied problems of Operations Research field, more specifically, in the Combinatorial Optimization field. As the TSP is a NP (Non-Deterministic Polynomial time)-hard... more

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A Multiagent Approach for Metaheuristics Hybridization Applied to the Traveling Salesman Problem

by Marco César Goldbarg

2021, 2012 Brazilian Symposium on Neural Networks

This paper proposes a multiagent approach for metaheuristics hybridization inspired on the popular technique called Particle Swarm Optimization (PSO). In the proposed approach, agents develop a society with collaboration to achieve their... more

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Multiagent optimization system

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