Abstract

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Highlights

  • In the past several decades, research on optimization has attracted more and more attention

  • The bacteria should always pursue the appropriate balance between exploration and exploitation of the foraging process in the search space, and this should be done by dynamically controlling parameters i.e., the bacterial runlength unit—C i that takes into account the current status of search i.e., the quality of the solutions

  • The flowchart of the ABFO1 algorithm can be illustrated by Figure 4 b, where S is the colony size, t is the chemotactic generation counter from 1 to max-generation, i is the bacterium’s ID counter from 1 to S, Xi is the ith bacterium’s position of the bacteria colony, Ns is the maximum number of steps for a single activity of Swim, and flagi is the number of generations the ith bacterium has not improved its own fitness

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Summary

Introduction

In the past several decades, research on optimization has attracted more and more attention. In order to improve the BFO’s performance on complex optimization problems with high dimensionality, we apply two natural foraging strategies, namely, the producerscrounger foraging PSF and the area concentrated search ACS , to the original BFO, resulting in two new adaptive bacterial foraging optimization models ABFOs , namely, ABFO1 and ABFO2. I a new adaptive strategy, namely, the producer-scrounger foraging, to dynamically determine the chemotactic step sizes for the whole bacterial colony during a run, dividing the foraging procedure of artificial bacteria colony into multiple explore and exploit phases; ii a new self-adaptive foraging strategy, namely, the area concentrate search, to respectively tune the chemotactic step size for each single bacterium during its run, casting the bacterial foraging process into heterogeneous fashion; iii a comprehensive study comparing ABFO1 and ABFO2 with another two state-ofthe-art global optimization algorithms, namely, GA and PSO, on high dimensional functions; iv single and colonial bacterial behaviors in both ABFO1 and ABFO2 that were simulated respectively in order to analyze in depth the adaptive and self-adaptive foraging schemes in the proposed models;.

The Classical BFO Algorithm
Chemotaxis
Reproduction
Elimination and Dispersal
Bacterial Behavior in BFO
Adaptive Strategies and Algorithms
The Producer-Scrounger Foraging
Area Concentrated Search
Taxonomy of Adaptation
Description of ABFO Algorithms
END IF
Griewank function f4 x
Parameter Settings for the Involved Algorithms
Results for the 2-D Problems
Results for the 10-D Problems
Results for the High-Dimensional Problems
Adaptive Bacterial Behaviors in ABFO Model
Conclusions
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