Ahamad Tajudin Khader

A comprehensive review

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.

A harmony search algorithm for university course timetabling

One of the main challenges for university administration is building a timetable for course sessions. This is not just about building a timetable that works, but building one that is as good as possible. In general, course timetabling is the process of assigning given courses to given rooms and timeslots under specific constraints. Harmony search algorithm is a new metaheuristic population-based algorithm, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments together seeking a pleasing harmony. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. In this paper, a harmony search and a modified harmony search algorithm are applied to university course timetabling against standard benchmarks. The results show that the proposed methods are capable of providing viable solutions in comparison to previous works.

University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm

University course timetabling problem (UCTP) is considered to be a hard combinatorial optimization problem to assign a set of events to a set of rooms and timeslots. Although several methods have been investigated, due to the nature of UCTP, memetic computing techniques have been more effective. A key feature of memetic computing is the hybridization of a population-based global search and the local improvement. Such hybridization is expected to strike a balance between exploration and exploitation of the search space. In this paper, a memetic computing technique that is designed for UCTP, called the hybrid harmony search algorithm (HHSA), is proposed. In HHSA, the harmony search algorithm (HSA), which is a metaheuristic population-based method, has been hybridized by: 1) hill climbing, to improve local exploitation; and 2) a global-best concept of particle swarm optimization to improve convergence. The results were compared against 27 other methods using the 11 datasets of Socha et al. comprising five small, five medium, and one large datasets. The proposed method achieved the optimal solution for the small dataset with comparable results for the medium datasets. Furthermore, in the most complex and large datasets, the proposed method achieved the best results.

Novel selection schemes for harmony search

Selection is a vital component used in Evolutionary Algorithms (EA) where the fitness value of the solution has influence on the evolution process. Normally, any efficient selection method makes use of the Darwinian principle of natural selection (i.e., survival of the fittest). Harmony search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Originally, two selection methods are used in HS: (i) memory consideration selection method where the values of the decision variables are randomly selected from the population (or solutions stored in harmony memory (HM)) to generate a new harmony, and (ii) selecting a new solution in HM whereby a greedy selection is used to update the HM. The memory consideration selection, the focal point of this paper, is not based on natural selection principle which draws heavily on random selection. In this paper, novel selection schemes which replace the random selection scheme in memory consideration are investigated, comprising global-best, fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of memory consideration and each adoption is realized as a new HS variation. The performance of the proposed HS variations are evaluated and a comparative study is conducted. The experimental results using benchmark functions show that the selection schemes incorporated in memory consideration directly affect the performance of HS algorithm. Finally, a parameter sensitivity analysis of the proposed HS variations is analyzed.

A Harmony Search with Multi-pitch Adjusting Rate for the University Course Timetabling

Course timetabling is a challenging administrative task for the educational institutions which have to painstakingly repeat the process several times per year. In general, course timetabling refers to the process of assigning given events to the given rooms and timeslots by taking into consideration the given hard and soft constraints. To tackle a highly-constraint timetabling problem, a powerful and robust algorithm that can deal with multidimensional gateways is required. Recently, the harmony search algorithm has been successfully tailored for the university course timetabling problem. In this chapter, the application of harmony search for the course timetabling is further enhanced by dividing the pitch adjustment operator to eight procedures, each of which is controlled by its PAR value range. Each pitch adjustment procedure is responsible for a particular local change in the new harmony. Furthermore, the acceptance rule for each pitch adjustment procedure is changed to accept the adjustment that leads to a better or equal objective function. Standard benchmarks are used to evaluate the proposed method. The results show that the proposed harmony search is capable of providing high-quality solutions compared to those in the previous works.

Selection mechanisms in memory consideration for examination timetabling with harmony search

In this paper, three selection mechanisms in memory consideration operator for Examination Timetabling Problem with Harmony Search Algorithm (HSA) are investigated: Random memory consideration which uses a random selection mechanism, global-best memory consideration which uses a selection mechanism inspired by a global best concept of Particle Swarm Optimisation (PSO), and Roulette-Wheel memory consideration which uses the survival for the fittest principle. The HSA with each proposed memory consideration operator is evaluated against a de facto dataset defined by Carter et al., (1996). The results suggest that the HSA with Roulette-Wheel memory consideration can produce good quality solutions. The Results are also compared with those obtained by 6 comparative methods that used Carter dataset demonstrating that the proposed method is able to obtain viable results with some best solutions for two testing datasets.

Island-based harmony search for optimization problems

Island model are embedded within framework of HS algorithm to build iHS.Island number ( I n ), migration frequency ( F m ), migration rate ( R m ) are iHS parameters.Higher values of I n and F m lead to better results but R m value needs to be small.Results outperform others due to manipulating diversity through island concepts. Harmony search (HS) algorithm is a recent meta-heuristic algorithm that mimics the musical improvisation concepts. This algorithm has been widely used for solving optimization problems. Moreover, many modifications in this algorithm have been carried out in order to improve the performance of the search. Island model is a structured population mechanism used in evolutionary algorithms to preserve the diversity of the population and thus improve the performance. In this paper, the island model concepts are embedded into the main framework of HS algorithm to improve its convergence properties where the new method is refer to as island HS (iHS). In the proposed method, the individuals in population are distributed into separate sub-population named (islands). Then the breeding loop is separately involved in each island. After specific generations, a number of individuals run an exchange through a process called migration. This process is performed to keep the diversity of population and to allow islands to interact with each other. The experimental result using a set of benchmark function shows that the island model context is crucial to the performance of iHS. Finally the sensitivity analysis and the comparative study for iHS prove the efficiency of the island model.

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

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.

An extended DEA windows analysis: Middle East and East African seaports

Purpose - The aim of this paper is to evaluate the efficiency of 22 cargo seaports situated in the regions of East Africa and Middle East. Design/methodology/approach - The data envelopment analysis (DEA) with window analysis model evaluates the efficiency score in terms of normal efficiency and super efficiency. The analysis is based on the panel data for the period of 2000-2005. Findings - The number of efficient decision making units (DMUs) under super efficiency is more than the number under normal efficiency. Originality/value - Using panel data, this paper is the first study to use super efficiency with window analysis that compares the efficiency estimated with the normal efficiency and with super efficiency.

A survey on applications and variants of the cuckoo search algorithm

Abstract This paper introduces a comprehensive and exhaustive overview of the cuckoo search algorithm (CSA). CSA is a metaheuristic swarm-based approach established by Yang and Deb [10] to emulate the cuckoo breeding behavior. Owing to the successful application of CSA for a wide variety of optimization problems, since then, researchers have developed several new algorithms in this field. This article displays a comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm. It is worth mentioning that the materials of this survey paper are categorized in accordance with the structure of the CSA in which the materials are divided into the CSA versions and modification, publication years, the CSA applications areas, and the hybridization of CSA. The survey paper ends with solid conclusions about the current research on CSA and the possible future directions for the relevant audience and readers. The researchers and practitioners on CSA belong to a wide range of audiences from the domains of optimization, engineering, medical, data mining, clustering, etc., who will benefit from this study.