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

Waqas Haider Bangyal,Abdul Hameed,Wael Alosaimi,Hashem Alyami,António Dourado

doi:10.1155/2021/6628889

Abstract

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.

Highlights

Optimization is considered the most productive field of research for many decades
Discussion. e purpose of this study is to observe the unique characteristics of the standard benchmark functions based on the dimensions of the experimental results. ree simulation tests were performed in the experiments, where the following TW-BA characteristics were observed: (i) Effect of using different initializing Particle swarm optimization (PSO) approaches (ii) Effect of using different dimensions for problems (iii) A comparative analysis e objective of this study was to find the most suitable initialization approach for the PSO and to explore Well Equi-distributed Long-period Linear (WELL)-based PSO initialization (WE-PSO) with other approaches, such as Sobol-based PSO (SO-PSO), Halton-based PSO (H-PSO), and standard PSO during the first experiment. e purpose of the second simulation is to define the essence of the dimension concerning the standard function optimization
Comparison of these training approaches is tested on real classification datasets that are taken from the UCI repository. e crossvalidation method is used to assess the efficiency of various classification techniques. e k-fold cross-validation method is used in this paper for the training of neural networks with the standard PSO, SO-PSO, H-PSO, and proposed algorithm WE-PSO. e k-fold is used with the value k 10 in the experiments. e dataset has been fragmented into 10 chunks; each data chunk comprises the same proportion of each class of dataset

Summary

Introduction

Optimization is considered the most productive field of research for many decades. E classification is an attempt to identify groups of certain categories of data. Data classification builds its model based on the genetic process and natural evolution [3]. E fundamental domain of artificial intelligence is swarm intelligence (SI), which discusses the developmental methods that govern the multiagent mechanism by systemic architecture and are influenced by the behaviour of social insects such as ants, wasps, bees, and termites. Researchers have been associated with social insect communities for decades, but for a long time, researchers have not established the composition of their collective behaviour. Complex tasks are accomplished effectively through an association with the single members of society as it strengthens the capacity to perform actions. In the field of optimization, different techniques of swarm intelligence are used

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Discussion

Conclusion