Enhanced Bacterial Foraging Optimization Based on Progressive Exploitation Toward Local Optimum and Adaptive Raid

Dongxing Wang,Yan Ma,Ziyi Lv,Xu Qian,Boyuan Ma,Xiaojuan Ban

doi:10.1109/access.2019.2927327

Abstract

Since the bacterial foraging optimization algorithm (BFO) was proposed, many variants about it have been designed in order to improve the performance and applied in different fields. Even so, people are constantly probing new methods designed to enhance the performance of BFO, so as to form new variants with superior performance. As a new variant of original BFO, bacterial foraging optimization using strategies of progressive exploitation approximating local optimum and adaptive raid (BFO-DX) was proposed. On the one hand, the strategy of progressive exploration approximating local optimum (PELO) was introduced into BFO to enhance its ability of exploitation in a local space, which enables the algorithm to find the global optima better possibly. On the other hand, the strategy of the adaptive raid for the leader (ARL) was adopted to boost the speed of convergence by strengthening its exploration capacity. The numerical experiments indicates that the BFO-DX possesses better ability of finding global optima, better stability and other acceptable terms such as iteration and running time compared with classical genetic algorithm (GA), particle swarm algorithm (PSO), and conventional bacterial foraging optimization (BFO).

Highlights

The metaheuristic search technology based on swarm intelligence has been increasing in popularity due to its ability to solve a variety of complex scientific and engineering problems [1]
On the basis of summarizing previous studies, especially referring to paper [3] and paper [7], this paper proposes a bacterial foraging optimization using strategies of progressive exploitation approximating local optimum and adaptive raid (BFO-DX)
NUMERICAL EXPERIMENTS In order to show the optimization performance of BFODX, comparisons between bacterial foraging optimization algorithm (BFO)-DX and some other state-ofthe-art algorithms including classical genetic algorithm(GA), particle swarm algorithm(PSO) and conventional BFO shown in TABLE 1 were made on six classical benchmarks, which are present as well as the order, function, expression, dimension, feature, range and the theoretical optimum about each of the test functions in TABLE 2, and in the two-dimensional form, Ackley [24] function is characterized by a nearly flat outer region and a large hole at the center, and it poses a risk for optimization algorithms, hill-climbing algorithms, to be trapped into one of its many local minima

Summary

INTRODUCTION

The metaheuristic search technology based on swarm intelligence has been increasing in popularity due to its ability to solve a variety of complex scientific and engineering problems [1] Such technology models the social behavior of certain living creatures, where each individual is simple, has limited cognitive capability and communicates only locally, but as a whole the swarm can act in a coordinated way and yield intelligent behavior to obtain global optima, especially, information exchange among individuals and balance between local exploitation and global exploration are two crucial mechanisms in different swarm intelligence algorithms [2]. On the basis of summarizing previous studies, especially referring to paper [3] and paper [7], this paper proposes a bacterial foraging optimization using strategies of progressive exploitation approximating local optimum and adaptive raid (BFO-DX)

CHEMOTAXIS

CLUSTERING

INITIALIZATION

NUMERICAL EXPERIMENTS