An Overview of Mutation Strategies in Bat Algorithm

Bangyal, Waqas Haider; Ahmad, Jamil; Tayyab, Hafiz; Pervaiz, Sobia

doi:10.14569/ijacsa.2018.090866

Cited by 10 publications

(1 citation statement)

References 44 publications

(52 reference statements)

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“…Immature performance explains the reason for an inadequate population distribution of the swarm. It often implies that optimum solutions are more difficult to find if the particles do not accurately cover the entire search space, which could omit the global optimum [ 13 – 15 ]. This issue can be resolved by introducing a well-organized random distribution to initialize the swarm.…”

Section: Introductionmentioning

confidence: 99%

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal

Hameed

Alosaimi

et al. 2021

Computational Intelligence and Neuroscience

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Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

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Section: Introductionmentioning

confidence: 99%