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A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems

by Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 13
Year of Publication: 2014
Authors: Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam
10.5120/16402-6079

Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam . A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems. International Journal of Computer Applications. 94, 13 ( May 2014), 15-20. DOI=10.5120/16402-6079

@article{ 10.5120/16402-6079,
author = { Md. Wasi Ul Kabir, Nazmus Sakib, Syed Mustafizur Rahman Chowdhury, Mohammad Shafiul Alam },
title = { A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 13 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number13/16402-6079/ },
doi = { 10.5120/16402-6079 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:32.768596+05:30
%A Md. Wasi Ul Kabir
%A Nazmus Sakib
%A Syed Mustafizur Rahman Chowdhury
%A Mohammad Shafiul Alam
%T A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 13
%P 15-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Swarm intelligence (SI) algorithms generally come from nature or biological behavior of nature. These algorithms use probabilistic search methods that simulate the behavior of biological entities or the natural biological evolution. Swarm intelligence (SI) is based on collective behavior of self-organized systems. Typical swarm intelligence algorithms include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Recently some new swarm based algorithms like Firefly Algorithm (FA) and Bat Algorithm (BA) has emerged. BA is a new optimization technique, which is based on the echolocation behavior of bats. BA is very efficient in exploitations but relatively poor in explorations. In this paper, a Novel Adaptive Bat Algorithm (NABA) is presented to improve the explorative characteristics of BA. The proposed algorithm incorporates two techniques within BA to improve its degree of explorations, which include the Rechenberg's 1/5 mutation rule and the Gaussian probability distribution to produce mutation step sizes. Both these techniques try to balance between the explorative and exploitative properties of BA. Simulation results on a number of benchmark functions on the continuous optimization problem suggest that the proposed algorithm – NABA often show much improved results, compared to the standard BA.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Optimization Bat Algorithm metaheuristics swam intelligence bio-inspired algorithm.

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