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Island-based Cuckoo Search with elite opposition-based learning and multiple mutation methods for solving optimization problems

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Abstract

The island Cuckoo Search (iCSPM) algorithm is a variation of Cuckoo Search that uses the island model and highly disruptive polynomial mutation to solve optimization problems. This article introduces an improved iCSPM algorithm called iCSPM with elite opposition-based learning and multiple mutation methods (iCSPM2). iCSPM2 has three main characteristics. Firstly, it separates candidate solutions into several islands (sub-populations) and then divides the islands among four improved Cuckoo Search algorithms: Cuckoo Search via Lévy flights, Cuckoo Search with highly disruptive polynomial mutation, Cuckoo Search with Jaya mutation and Cuckoo Search with pitch adjustment mutation. Secondly, it uses elite opposition-based learning to improve its convergence rate and exploration ability. Finally, it makes continuous candidate solutions discrete using the smallest position value method. A set of 15 popular benchmark functions indicate iCSPM2 performs better than iCSPM. However, based on sensitivity analysis of both algorithms, convergence behavior seems sensitive to island model parameters. Further, the single-objective IEEE-CEC 2014 functions were used to evaluate and compare the performance of iCSPM2 to four well-known swarm optimization algorithms: distributed grey wolf optimizer, distributed adaptive differential evolution with linear population size reduction evolution, memory-based hybrid dragonfly algorithm and fireworks algorithm with differential mutation. Experimental and statistical results suggest iCSPM2 has better performance than the four other algorithms. iCSPM2’s performance was also shown to be favorable compared to two powerful discrete optimization algorithms (generalized accelerations for insertion-based heuristics and memetic algorithm with novel semi-constructive crossover and mutation operators) using a set of Taillard’s benchmark instances for the permutation flow shop scheduling problem.

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Authors and Affiliations

  1. Department of Computer Sciences, Yarmouk University, Irbid, Jordan

    Bilal H. Abed-alguni

  2. School of Science and Technology, The University of New England, Armidale, NSW, Australia

    David Paul

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  1. Bilal H. Abed-alguni
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BHA contributed to conceptualization, methodology, investigation, validation, writing, experimentation, visualization, reviewing and editing. DP performed writing, reviewing and editing.

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Correspondence to Bilal H. Abed-alguni.

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Abed-alguni, B.H., Paul, D. Island-based Cuckoo Search with elite opposition-based learning and multiple mutation methods for solving optimization problems. Soft Comput 26, 3293–3312 (2022). https://doi.org/10.1007/s00500-021-06665-6

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