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A Hybrid CSPSO Algorithm for Global Optimization

A Hybrid CS/PSO Algorithm for Global Optimization

Amirhossein Ghodrati1 and Shahriar Lotfi2

1 Computer Engineering Department, College of Nabi Akram, Tabriz, Iran

2 Computer Science Department, University of Tabriz, Tabriz, Iran

a.h.ghodrati@http://www.wendangxiazai.com, shahriar_lotfi@tabrizu.ac.ir

Abstract. This paper presents the hybrid approach of two nature inspired

metaheuristic algorithms; Cuckoo Search (CS) and Particle Swarm

Optimization (PSO) for solving optimization problems. Cuckoo birds lay their

own eggs to other host birds. If the host birds discover the alien birds, they will

leave the nest or throw the egg away. Cuckoo birds migrate to the environments

that reduce the chance of their eggs to be discovered by the host birds. In

standard CS, cuckoo birds experience new places by the Lévy Flight. In the

proposed hybrid algorithm, cuckoo birds are aware of each other positions and

make use of swarm intelligence in PSO in order to reach to better solutions.

Experimental results are examined with some standard benchmark functions

and the results show a promising performance of this algorithm.

Keywords: Cuckoo Search, Global optimization, PSO, Metaheuristic and

Hybrid evolutionary algorithm.

1Introduction

Finding optimal solutions for many problems is very difficult to deal with. The complexity of such problems makes it impossible to look for every possible solution or combination [1]. However, because of their complexity the use of approximation algorithms in order to find approximate solutions is getting more popular in the past few years [2]. Among these algorithms, modern metaheuristics are becoming popular, which leads to a new branch of optimization, named metaheuristic optimization. Most of these algorithms are nature inspired [3], some of which have been proposed for optimization problems, for example, Genetic Algorithm (GA) [4], Harmony Search (HS) [5], Ant Colony Optimization (ACO) [6], Imperialist Competitive Algorithm (ICA) [7] and Artificial Bee Colony [8].

Yang and Deb formulated a new metaheuristic algorithm, called Cuckoo Search in 2009 [9]. This algorithm is inspired by life of cuckoo bird in combination with Lévy Flight behavior of some birds and fruit flies. The studies show the CS algorithm is very promising and could outperform some known algorithms, such as PSO and GA.

PSO was formulated by Kennedy and Eberhart in 1995 [10]. It is an evolutionary computation technique which is inspired by social behavior of swarms. This algorithm is the simulation of the social behavior of birds, like the choreography of a bird flock. Each individual in the population is a particle and gets a random value in J.-S. Pan, S.-M. Chen, N.T. Nguyen (Eds.): ACIIDS 2012, Part III, LNAI 7198, pp. 89–98, 2012.

© Springer-Verlag Berlin Heidelberg 2012

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