Impact of Chaos Functions on Modern Swarm Optimizers

Emary, Eid; Zawbaa, Hossam M.

doi:10.1371/journal.pone.0158738

Cited by 68 publications

(25 citation statements)

References 30 publications

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“…The experimental results on 19 unconstrained benchmark functions and two other constrained problems showed the superior performance of LMFO. Emery et al [5] employed chaos parameter in the spiral equation of updating the position of moths. They applied their approach in feature selection application.…”

Section: B Gehad Ismail Sayedmentioning

confidence: 99%

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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Section: B Gehad Ismail Sayedmentioning

confidence: 99%

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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“…Emary et al [44] used three modern techniques, namely Antlion Optimizer, MFO and GWO in domain of machine learning for feature selection. Solutions on a set of standard machine learning data using a set of assessment indicators proved advances in optimization approach performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Singh

Hachimi

2018

MCA

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Abstract:The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

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“…Most of the evolutionary algorithms include in their mathematical formulation a parameter that is randomly changed during the execution of the method. A study on the impact of using chaotic parameters instead of random parameters in evolutionary algorithms is presented in [16]. Many works have been published reporting the successful use of chaotic mechanisms in different algorithms such as Symbiotic Organisms Search (SOS) [17], Firefly algorithm [18], Whale Optimization [19], and Teaching-Learning Based Optimization (TLBO) [20].…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

Rodrigues¹,

Gomes²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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The Grey Wolf Optimizer (GWO) algorithm is a nature-inspired population-based metaheuristic that simulates the social behavior observed in a grey wolf pack. GWO has been successfully applied to different optimization problems. In this paper, we propose a chaotic version of GWO, denoted by CGWO, that uses a chaotic variable to define the number of wolves in the pack that will act as leaders, i.e. the number of wolves that guide the hunting process in each iteration of the algorithm. The proposed algorithm is used to find the optimal maintenance scope for a series-parallel system. We assume that a Prognostics and Health Management (PHM) system is available and provides the degradation level and the Remaining Useful Life (RUL) prediction for each component. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance intervention. The performance of the proposed model is compared with the performance of the original GWO and the well-studied Ant Colony Optimization algorithm (ACO). Different chaotic maps were tested. The results show that the proposed model presented a competitive performance.

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Impact of Chaos Functions on Modern Swarm Optimizers

Cited by 68 publications

References 30 publications

A hybrid SA-MFO algorithm for function optimization and engineering design problems

A hybrid SA-MFO algorithm for function optimization and engineering design problems

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

A Chaotic Grey Wolf Optimizer Applied to Condition-Based Maintenance Optimization

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