Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Abstract

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

“…In Formula (10) e random range is [0, 1]. In the improved gray wolf optimization algorithm, the competitive cooperation between wolf leaders is incorporated to improve the overall search performance [18]. e division and update of the wolf leader level would be determined by the real-time error evaluation value, and the active area of the wolf group would be determined by Formulas ( 11)-( 13):…”

Umbrellalike Hierarchical Artificial Bee Colony Algorithm

“…Due to its unpredictable behavior, particularly at high dimensions, F2 was removed [45]. These benchmark functions are frequently employed, which is sufficient to confirm the effectiveness of an optimization strategy from multiple perspectives [46]. We established three distinct function dimensions (D), D = 30, 50 and 100.…”

A Non-Revisiting Equilibrium Optimizer Algorithm

“…Many studies in the field of evolutionary algorithms have demonstrated that historical information generated by population iterations has a favorable influence on new individual variation [16], [28]. Therefore, with the fundamental aim of further increasing the weight of historical information in individual variation while avoiding over-reliance on specific individuals, an archive population (S) independent of the main population (C) is designed in this study.…”