Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search.

Xingwang Huang,Rui Han,Xuewen Zeng

doi:10.1155/2017/3235720

Abstract

Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.

Highlights

There are many optimization problems with binary search space
Mirjalili et al [8] adapted the standard continuous bat algorithm (BA) algorithm to be applied to binary spaces and Binary bat algorithm (BBA) combined with k-Nearest Neighbor (KNN, k = 1) method was used to solve feature selection problem [2]
(2) To evaluate its performance, the proposed improved binary bat algorithm (IBBA) and several other algorithms are implemented on benchmark functions and zero-one knapsack problems

Summary

Introduction

There are many optimization problems with binary search space. And many of them are high dimensional. Mirjalili et al [8] adapted the standard continuous BA algorithm to be applied to binary spaces and BBA combined with k-Nearest Neighbor (KNN, k = 1) method was used to solve feature selection problem [2]. BBA can provide competitive performance but, in some cases, it may get stuck to local minima. To solve this issue, an improved binary bat algorithm, named IBBA, is proposed. (1) An improved high-performance binary bat algorithm is proposed for binary problems. (2) To evaluate its performance, the proposed IBBA and several other algorithms are implemented on benchmark functions and zero-one knapsack problems.

Background

Improved Binary Bat Algorithm

Benchmark Experiment

Zero-One Knapsack Problem

Discussion

Conclusions