Bolaji Asaju

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Address: Ilorin, Kwara State, Nigeria

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Ayotunde:Animproved versionofLeasys:anintelligentplantidentificationsystem An improved version of Leasys: an intelligent plant identification system

A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

Journal of Intelligent Systems, 2015

This article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetab... more This article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than...

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Breakdown Analysis of Pearson Correlation Coefficient and Robust Correlation Methods

IOP Conference Series: Materials Science and Engineering, 2020

This paper discussed plug in robust procedure to robustify the Pearson product moment correlation... more This paper discussed plug in robust procedure to robustify the Pearson product moment correlation coefficient (PPMCC). The mean of PPMCC is highly susceptible to influential observations hence the PPMCC is not robust against data set that contains substantial amount of influential observations. This study focused on robust plug in techniques with high breakdown points. The performance of these techniques are compared using real and simulated data set. The comparative analysis indicates different degrees of robustness and breakdown based on the percentage of contamination and data modification.

Nurse Rostering Using Modified Harmony Search Algorithm

Lecture Notes in Computer Science, 2011

ABSTRACT In this paper, a Harmony Search Algorithm (HSA) is adapted for Nurse Rostering Problem (... more ABSTRACT In this paper, a Harmony Search Algorithm (HSA) is adapted for Nurse Rostering Problem (NRP). HSA is a global optimization method derived from a musical improvisation process which has been successfully tailored for several optimization domains. NRP is a hard combinatorial scheduling problem of assigning given shifts to given nurses. Using a dataset established by International Nurse Rostering Competition 2010 of sprint dataset that has 10-early, 10-late, 10-hidden, and 3-hint. The proposed method achieved competitively comparable results.

Hybrid Harmony Search for Nurse Rostering Problems

2013 IEEE Symposium on Computational Intelligence in Scheduling (CISched), 2013

ABSTRACT In this paper, a Hybrid Harmony Search Algorithm (HHSA) is presented for Nurse Rostering... more ABSTRACT In this paper, a Hybrid Harmony Search Algorithm (HHSA) is presented for Nurse Rostering Problem (NRP) using the dataset proposed by the First International Nurse Rostering Competition (INRC2010). NRP is tackled by assigning daily shifts to nurses with different skills and working contracts, subject to hard and soft constraints. Harmony Search Algorithm (HSA) is a recent evolutionary computing technique, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments. Recently, HSA has been used for NRP, with promising results. This paper extends HSA to HHSA by adding two powerful concepts to HSA: (i) hybridization with hill climbing optimizer to improve the exploitation ability, and (ii) hybridization with global-best concept of particle swarm optimization to improve the speed of convergence. The proposed HHSA is evaluated against a dataset provided by INRC2010. The results show that it is a powerful technique for INRC2010 dataset. A comparative analysis with five competitive methods is conducted. HHSA outperforms the other competitive methods in three instances and obtained the best results in 29 others out of 69 instances. The efficiency of our method lends further support to the previous theory based on hybridizing the local search within evolutionary computing technique for hard combinatorial optimization problems.

Nurse Scheduling Using Harmony Search

… and Applications (BIC …, 2011

... Mohammed A. Awadallah1, Ahamad Tajudin Khader1, Mohammed Azmi Al-Betar1,2, Asaju La&amp;a... more ... Mohammed A. Awadallah1, Ahamad Tajudin Khader1, Mohammed Azmi Al-Betar1,2, Asaju La&amp;amp;amp;amp;#x27;aro Bolaji1 1 School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Pulau Pinang, Malaysia. ... If the new har-mony vector, x = (x1,x2, ··· ,xN ), is better than the worst ...

Harmony Search with Novel Selection Methods in Memory Consideration for Nurse Rostering Problem

Asia-Pacific Journal of Operational Research, 2013

The selection methods of population-based metaheuristics provide the driving force to generate go... more The selection methods of population-based metaheuristics provide the driving force to generate good solutions. These selection methods select the individuals with a higher fitness to be members of the population in the next iteration correspond to the natural rule of Darwin's principle survival-of-the-fittest. Harmony search algorithm is a population-based metaheuristic, which mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments seeking for a pleasing harmony. It improvises the new harmony based on three rules: memory consideration, random consideration, and pitch adjustment. In this paper, we investigate the replacement of the original random selection of memory consideration with a set of selection methods in order to speed-up the convergence. These selection methods include tournament, proportional, and liner rank of Genetic Algorithm, and Global-best of Particle Swarm Optimization. The proposed harmony search wi...

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A Modified Artificial Bee Colony Algorithm for Post-enrolment Course Timetabling

Lecture Notes in Computer Science, 2013

Tournament-based harmony search algorithm for non-convex economic load dispatch problem

Applied Soft Computing, 2016

A comprehensive review: Krill Herd algorithm (KH) and its applications

Applied Soft Computing, 2016

Hybridization of harmony search with hill climbing for highly constrained nurse rostering problem

Neural Computing and Applications, 2015

A hybrid artificial bee colony for a nurse rostering problem

Applied Soft Computing, 2015

Solving Nurse Rostering Problem Using Artificial Bee Colony Algorithm

The 7th International Conference on Information Technology, 2015

Artificial bee colony algorithm(ABC) is proposed as a new nature-inspired algorithm which has bee... more Artificial bee colony algorithm(ABC) is proposed as a new nature-inspired algorithm which has been successfully utilized to tackle numerous class of optimization problems belongs to the category of swarm intelligence optimization algorithms. The major focus of this paper is to show that ABC could be used to generate good solutions when adapted to tackle the nurse rostering problem (NRP). In the proposed ABC for the NRP, the solution methods is divided into two phases. The first uses a heuristic ordering strategy to generate feasible solutions while the second phase employs the usage of ABC algorithm in which its operators are utilized to enhance the feasible solutions to their optimality. The proposed algorithm is tested on a set of 69 problem instances of the dataset introduced by the First International Nurse Rostering Competition 2010 (INRC2010). The results produced by the proposed algorithm are very promising when compared with some existing techniques that worked on the same dataset. Further investigation is still necessary for further improvement of the proposed algorithm.

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An improved version of Leasys: an intelligent plant identification system

Journal of Agricultural Informatics, 2012

In an attempt to make identification of plants easy and less cumbersome, computer-based software ... more In an attempt to make identification of plants easy and less cumbersome, computer-based software called Leasys was developed. It is a computerized version of a field key prepared for on-the-spot identification of savanna tree species in Nigeria. Leasys 1.1 is an improved version of the earlier version developed by the same authors. The current version is more expansive than the first version and thus has more features which make identification process faster and easier. Incorporated in Leasys 1.1 are some other features that make it more robust, flexible and versatile. These features include Leasys Property Registration which makes identification of a plant along two selection paths possible, Leasys Plant Management which inserts, updates and deletes plant data from the database, Leasys Deduction System whicht enables the user identify a plant by inputting a leaf feature and Leasys Quick Search which brings about fast identification of plant without going through the step by step process of identification. It is also possible to print one or all results generated. Leasys has a database on two hundred (200) different savanna trees. The system has the potential and capacity to accommodate very large number of species and very large number of taxonomic characters. With the implementation of a specialized algorithm based on the SQL OLEDB engine, AND operator professional reporting system, impressive nature GUI's network compatibility and an enhanced interaction level, Leasys has the great potengtial for progressive upgrading.

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Artificial Bee Colony Algorithm, Its Variants and Applications: A Survey

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foragi... more Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. The advantages, applications and drawbacks of the newly developed ABC hybrids are highlighted, critically analyzed and discussed accordingly.

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Global best Harmony Search with a new pitch adjustment designed for Nurse Rostering

Journal of King Saud University - Computer and Information Sciences, 2013

In this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Probl... more In this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Problem (NRP) using a dataset introduced in the First International Nurse Rostering Competition (INRC2010). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a predefined scheduling period. HSA is an approximation method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. It improvises the new harmony iteratively using three operators: memory consideration, random consideration, and pitch adjustment. Recently, HSA has been used for NRP, with promising results. This paper has made two major improvements to HSA for NRP: (i) replacing random selection with the Global-best selection of Particle Swarm Optimization in memory consideration operator to improve convergence speed. (ii) Establishing multi-pitch adjustment procedures to improve local exploitation. The result obtained by HSA is comparable with those produced by the five INRC2010 winners' methods.

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A Modified Artificial Bee Colony Algorithm for Post-enrolment Course Timetabling

Lecture Notes in Computer Science, 2013

Harmony Search with Greedy Shuffle for Nurse Rostering

International Journal of Natural Computing Research, 2012

In this paper, a hybridization of Harmony Search Algorithm (HSA) with a greedy shuffle move is pr... more In this paper, a hybridization of Harmony Search Algorithm (HSA) with a greedy shuffle move is proposed for Nurse Rostering Problem (NRP). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a pre-determined scheduling period. HSA is a population-based method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. The performance of HSA is enhanced by hybridizing it with a greedy shuffle move. The proposed method is evaluated using a dataset defined in first International Nurse Rostering Competition (INRC2010). The hybrid HSA obtained the best results of the comparative methods in four datasets.

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Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems

International Journal of Natural Computing Research, 2012

This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (E... more This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

Tackling University Course Timetabling Problem Using Artificial Bee Colony Algorithm

masaumnet.com

This research article presents an Artificial Bee Colony algorithm for solving timetabling problem... more This research article presents an Artificial Bee Colony algorithm for solving timetabling problems, with emphasis on curriculum-based course timetabling. An attempt was made to solve these problems via an approach broken down into two parts; first, Saturation Degree (SD) was used to ensure feasible solution, where the hard constraints were satisfied. In the second part, Artificial Bee Colony Algorithm (ABC) was used to improve the results obtained. The algorithm produced good results, though they were not better when compared to those already reported in the literature because ABC easily gets stuck in the local optimal solution. When properly modified through the hybridization of local search-based algorithms, this approach could boost ABC to perform better, to solve timetabling problems in general.

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