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Dive into the research topics where Asaju La’aro Bolaji is active.

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Featured researches published by Asaju La’aro Bolaji.


Applied Soft Computing | 2016

A comprehensive review

Asaju La’aro Bolaji; Mohammed Azmi Al-Betar; Mohammed A. Awadallah; Ahamad Tajudin Khader; Laith Mohammad Abualigah

Graphical abstractDisplay Omitted HighlightsThe comprehensive review of Krill Herd Algorithm as applied to different domain is presented.The review covers the applications, modifications and hybridizations of the KH algorithms.It provides future research directions across different areas. Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed.


Applied Soft Computing | 2016

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

Mohammed Azmi Al-Betar; Mohammed A. Awadallah; Ahamad Tajudin Khader; Asaju La’aro Bolaji

Graphical abstractDisplay Omitted HighlightsThe tournament-based harmony search (THS) algorithm is proposed for economic load dispatch (ELD) problem.In THS, the random selection in the memory consideration is replaced by the tournament selection to observe the natural selection strategy.The proposed THS is tested using three different tournament size values for four test ELD systems.The results suggest that the THS with higher tournament size is efficient for ELD.New results appeared using THS for ELD when THS was compared with 43 methods published in 33 articles. This paper proposes a tournament-based harmony search (THS) algorithm for economic load dispatch (ELD) problem. The THS is an efficient modified version of the harmony search (HS) algorithm where the random selection process in the memory consideration operator is replaced by the tournament selection process to activate the natural selection of the survival-of-the-fittest principle and thus improve the convergence properties of HS. The performance THS is evaluated with ELD problem using five different test systems: 3-units generator system; two versions of 13-units generator system; 40-units generator system; and large-scaled 80-units generator system. The effect of tournament size (t) on the performance of THS is studied. A comparative evaluation between THS and other existing methods reported in the literature are carried out. The simulation results show that the THS algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.


Applied Soft Computing | 2015

A hybrid artificial bee colony for a nurse rostering problem

Mohammed A. Awadallah; Asaju La’aro Bolaji; Mohammed Azmi Al-Betar

Graphical abstractDisplay Omitted HighlightsProposed a hybrid artificial bee colony (HABC) algorithm for a nurse rostering problem (NRP).Compared the HABC results with other eleven comparative methods using INRC2010 dataset.Showed that HABC algorithm performs well.This research showed that a well-designed hybrid technique is a competitive alternative for solving NRP. The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.


Journal of Computational Science | 2014

University course timetabling using hybridized artificial bee colony with hill climbing optimizer

Asaju La’aro Bolaji; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Mohammed A. Awadallah

University course timetabling is concerned with assigning a set of courses to a set of rooms and timeslots according to a set of constraints. This problem has been tackled using metaheuristics techniques. Artificial bee colony (ABC) algorithm has been successfully used for tackling uncapaciated examination and course timetabling problems. In this paper, a novel hybrid ABC algorithm based on the integrated technique is proposed for tackling the university course timetabling problem. First of all, initial feasible solutions are generated using the combination of saturation degree (SD) and backtracking algorithm (BA). Secondly, a hill climbing optimizer is embedded within the employed bee operator to enhance the local exploitation ability of the original ABC algorithm while tackling the problem. Hill climbing iteratively navigates the search space of each population member in order to reach a local optima. The proposed hybrid ABC technique is evaluated using the dataset established by Socha including five small, five medium and one large problem instances. Empirical results on these problem instances validate the effectiveness and efficiency of the proposed algorithm. Our work also shows that a well-designed hybrid technique is a competitive alternative for addressing the university course timetabling problem.


Journal of intelligent systems | 2015

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

Asaju La’aro Bolaji; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Mohammed A. Awadallah

Abstract 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 exiting ABC techniques and competes well with other techniques from the literature.


bio-inspired computing: theories and applications | 2011

Nurse Scheduling Using Harmony Search

Mohammed A. Awadallah; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Asaju La’aro Bolaji

In this research an adaption of Harmony Search Algorithm (HSA) for Nurse Scheduling Problem (NSP) is presented. Nurse scheduling problem is a task of assigning shifts to nurses for the duties that have to carry out. The difficulty of handling this problem is due to the high number of constraints to be satisfied. Thus, we are proposing an adaptation of HSA i.e. a new population-based metaheuristic algorithm that mimics the musical improvisation process which has been successfully applied for wide range of optimisation problems. The performance of HAS is evaluated using datasets established by International Nurse Rostering Competition 2010 (INRC2010). The results obtained were compared with the best results reported in the competition. The results show that the proposed method can compete well in comparison with those reported results.


Neural Computing and Applications | 2017

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

Mohammed A. Awadallah; Mohammed Azmi Al-Betar; Ahamad Tajudin Khader; Asaju La’aro Bolaji; Mahmud S. Alkoffash

Abstract This paper proposes a hybrid harmony search algorithm (HHSA) for solving the highly constrained nurse rostering problem (NRP). The NRP is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses; each has specific skills and work contract, to a predefined rostering period according to a set of constraints. The harmony search is a metaheuristic approach, where the metaheuristics are the most successful methods for tackling this problem. In HHSA, the harmony search algorithm is hybridized with the hill climbing optimizer to empower its exploitation capability. Furthermore, the memory consideration operator of the HHSA is modified by replacing the random selection scheme with the global-best concept of particle swarm optimization to accelerate its convergence rate. The standard dataset published in the first international nurse rostering competition 2010 (INRC2010) was utilized to evaluate the proposed HHSA. Several convergence scenarios have been employed to study the effects of the two HHSA modifications. Finally, a comparative evaluation against twelve other methods that worked on the INRC2010 dataset is carried out. The experimental results show that the proposed method achieved five new best results, and 33 best published results out of 69 instances as achieved by other comparative methods.


bio-inspired computing: theories and applications | 2011

An Improved Artificial Bee Colony for Course Timetabling

Asaju La’aro Bolaji; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Mohammed A. Awadallah

The Artificial Bee Colony Algorithm (ABC) is an emerging nature-inspired, metaheuristic optimisation algorithm. In this paper, an improved ABC algorithm is proposed for tackling Curriculum-Based Course Timetabling Problem (CBCTT). The ABC as a population-based algorithm, the initial population is generated using Saturation Degree (SD) followed by Backtracking Algorithm (BA) to ensure that all the solutions in the population are feasible. The improvement loop in ABC used neighbourhood structures severally within the employed and onlooker bees operators in order to navigate the CB-CTT search space tightly. The performance of ABC is tested using dataset prepared by second international timetabling competition (ITC-2007), the ABC is able to achieved good quality results, yet these are not comparable with the best results obtained by other methods. Future work can be directed further improve the ABC operators to achieve a better results.


Neural Computing and Applications | 2018

Economic load dispatch problems with valve-point loading using natural updated harmony search

Mohammed Azmi Al-Betar; Mohammed A. Awadallah; Ahamad Tajudin Khader; Asaju La’aro Bolaji; Ammar Almomani

In this paper, the update process of harmony search (HS) algorithm is modified to improve its concept of diversity. The update process in HS is based on a greedy mechanism in which the new harmony solution, created in each generation, replaces the worst individual in the population, if better. This greedy process could be improved with other updates mechanisms in order to control the diversity perfectly. Three versions of HS have been proposed: (1) Natural Proportional HS ; (2) Natural Tournament HS; (3) Natural Rank HS. These three HS versions employed the natural selection principle of the “survival of the fittest”. Instead of replacing the worst individual in population, any individual can be replaced based on certain criteria. Four versions of economic loading dispatch (ELD) problems with valve point have been used to measure the effect of the newly proposed HS versions. The results show that the new HS versions are very promising for ELD domain. This claim is proved based on the comparative evaluation process where the new HS versions are able to excel the state-of-the-art methods in almost ELD problems used.


Asia-Pacific Journal of Operational Research | 2014

HARMONY SEARCH WITH NOVEL SELECTION METHODS IN MEMORY CONSIDERATION FOR NURSE ROSTERING PROBLEM

Mohammed A. Awadallah; Ahamad Tajudin Khader; Mohammed Azmi Al-Betar; Asaju La’aro Bolaji

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 Darwins 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 with the different memory consideration selection methods evaluated using standard dataset published in the first International Nurse Rostering Competition INRC2010. Nurse rostering problem is a combinatorial optimization problem tackled by assigning a set of nurses with different skills to a set of shifts over predefined scheduling period. Experimentally, the tournament memory consideration selection method achieved the best rate of convergence as well as the best results in comparison with the other memory consideration selection methods.

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