Multiple Chaos Embedded Gravitational Search Algorithm

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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.

Section: Introductionmentioning

confidence: 89%

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

Yang

et al. 2021

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“…To further verify the performance of GSA-CNO1, four GSA variants including GSA [19], CGSA [34], PSOGSA [36] and CGSA-M [35] are adopted. Their comparisons are conducted on twenty-nine CEC2017 benchmark functions with 30 and 50 dimensions, respectively.…”

Section: Comparison For Gsa Variantsmentioning

confidence: 99%

“…In [34], chaotic maps were used to adjust the gravitational constant. In [35], chaotic local search was employed to further exploit global optimal solutions. Hybrid strategies apply characteristics of several approaches to tackle the limitation of GSA.…”

Section: Introductionmentioning

confidence: 99%

A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang

Gao

et al. 2020

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Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

“…Their specific details can be referred in [47]. Seven comparative algorithms including WOA [48], DE [46], CGSA-P [49], BSO [15], EPSDE [50], SaDE [51] and jDE [52] are used. Their parameter settings are adopted according to corresponding literatures.…”

Section: Three Real-world Optimization Problemsmentioning

confidence: 99%

TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

Cai

Wang

et al. 2020

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Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.