Abstract
In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. The ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. These two phases are tied with a bridging mechanism, which plays an important role. This paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. This experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). The performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.
Highlights
Optimization is a term which refers to the selection of the best option amongst the given set of alternatives
Unimodal functions are the functions which have no local minima, or in other words, they possess only one minima. ese functions are suitable for benchmarking the exploitation quality and convergence speed of any algorithm. is section presents the results on unimodal benchmark problems for 30 dimensions and 50 dimensions
Exploration and exploitation phases of a metaheuristic algorithm are connected with a bridging mechanism. e efficacy of this bridging mechanism is important to achieve better convergence characteristics, solution quality, and optimization performance. is paper focuses on this mechanism, and 10 chaotic bridging mechanisms have been proposed for Grasshopper Optimisation Algorithm (GOA)
Summary
Optimization is a term which refers to the selection of the best option amongst the given set of alternatives. The use of maximization is for efficiency maximization, classification accuracy maximization, and revenue or profit maximization, and on the other hand, minimization can be performed for cost, loss, risk, and execution time of any engineering process. Apart from these classifications of optimization, another classification of the optimization problem can be done on the basis of constraints. A recent trend is to employ metaheuristic optimization algorithms to solve challenging problems of the real world. Applications of metaheuristic algorithms in engineering problems have been reported. Applications of metaheuristic algorithms in engineering problems have been reported. e successful and effective implementation of these algorithms on real applications has attracted the attention of researchers to work in this direction
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