The Role of Metaheuristics as Solutions Generators

Metaheuristics are a wide class of solution methods that have been successfully applied to many optimization problems. The assessment of these methods is commonly based on experimental analysis but the lack of a methodology in these analyses limits the scientific value of their results. In this paper we formalize different scenarios for the analysis and comparison of metaheuristics by experimentation. For each scenario we give pointers to the existing statistical methodology for carrying out a sound analysis. Finally, we provide a set of open issues and further research directions.

Nonconvex Optimization and Its Applications, 2006

TheScientificWorldJournal, 2015

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

Artificial Intelligence Review, 2018

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a "black box" that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions - few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (thirty three years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

2020

Engineering optimization problems can be dominated by inconsistent performance requirements and incompatible specifications that can be difficult to detect when supporting mathematical programming models are formulated. Thus, it often proves advantageous to construct a set of options that provide dissimilar approaches to such problems. These alternatives should satisfy the required performance criteria, but be maximally different from each other in their decision spaces. The method for constructing maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This paper considers a multicriteria method that can generate sets of maximally different alternatives using any population-based solution algorithm. This MGA approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The computational efficacy of this multicriteria M...

Metaheuristics have often been shown to be effective for difficult combinatorial optimization problems. The reason for that, however, remains unclear. A framework for a theory of metaheuristics crucially depends on a formal representative model of such algorithms. This paper unifies/reconciles in a single framework the model of a black box algorithm coming from the no-free-lunch research (e.g. Wolpert et al.

2006

This paper is concerned with an aspect of the design of metaheuristic algorithms, such as evolutionary algorithms, tabu search and ant colony optimization. The topic that is considered is how problems can be represented when they are given to a metaheuristic algorithm. A particular difficulty is presented, viz. the''bottleneck'', where the problem is artificially converted into a new representation in order to fit the standard input to the metaheuristic.

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

International Series in Operations Research & Management Science, 2003

Operations Research/Computer Science Interfaces Series, 2002

Essays and surveys in metaheuristics / [edited by] Celso C. Ribeiro, Pierre Hansen, p. cm.-(Operations research/computer science interfaces series ; ORCS 15) Includes bibliographical references.