A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

Wali Khan Mashwani,Ismail Koçcak,Ihsan Mehmood,Maharani Abu Bakar,Hassène Gritli

doi:10.1155/2021/6636918

Abstract

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.

Highlights

Global optimization has wide applications in many fields including physics, biology, engineering, economics, and business
Several continuous test suites of benchmark functions have already been designed in the existing Evolutionary computation (EC) community over the last few years. ese benchmark functions have played a crucial role in developing and scientifically studying the behavior of various existing evolutionary algorithms (EAs) and modified enhanced versions. e benchmark functions which have recently been designed for the special session of the 2005 IEEE Congress on Evolutionary Computation are utilized in the study conducted in this paper in order to carefully assess the proposed modified Bat algorithm (MBA) and impact analysis of various temperatures used in its framework
For more details about these instances, the readers are referred to [51]. ese benchmark functions that can be categorized as F1 to F5 are unimodal functions, F6 to F12 are basic multimodal functions, F13 and F14 are both expanded multimodal benchmark functions, and F15 is a hybrid composition test problem

Summary

Introduction

Global optimization has wide applications in many fields including physics, biology, engineering, economics, and business. Examples include minimizing the cost/consumption, while maximizing the profit/product is the need of the day in every time which leads human being towards the optimization process. It has been an essential part of research in applied mathematics since six or seven decades. E advancement of computers makes it possible to solve those optimization problems that were unsolvable before. E main objective of the optimization is to maximize the efficiency of the system while minimizing its cost. A scalable optimization problem can typically and generally be formulated as follows: Minimize f(x) f1(x), f2(x), . In the last few decades, different kinds of linear and nonlinear optimization techniques were developed to solve various linear and nonlinear optimization problems [1,2,3,4,5]

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Results

Conclusion