A hyper-heuristic for improving the initial population of whale optimization algorithm

This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (O...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 172; pp. 42 - 63
Main Authors: Elaziz, Mohamed Abd, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15.05.2019
Elsevier Science Ltd
Subjects:
Algorithms
Computer simulation
Differential Evolution (DE)
Evolutionary algorithms
Evolutionary computation
Experiments
Global optimization
Heuristic
Heuristic methods
Machine learning
Optimization
Optimization algorithms
Performance enhancement
Swarm intelligent
Whale Optimization Algorithm (WOA)
Whales & whaling
Swarm intelligent
Global optimization
Whale Optimization Algorithm (WOA)
Differential Evolution (DE)
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.02.010