A Comparative Study of Various Metaheuristic Algorithms

Nature inspired algorithms are used over the passed decade that provide efficient methods to resolve optimization problem that where not possible with the conventional methods. The main advantage with these algorithms is that they perform iterative searches efficiently in this context we present comparative analysis of four metaheuristic algorithms namely –Firefly , Bat, Cuckoo search and Krill herd is presented in this paper. The analyses is presented based on its Area of application its characteristics Year of development etc. KeywordsFirefly, Bat, Cuckoo search, Krill herd, Optimization I.INTRODUCTION Natures inspired algorithms are widely used to find best solutions to various optimization problems. Some of the examples of them are genetic algorithm[1], and ACO[2], simulated annealing[3], differential evolution[4], PSO[5] and BCO[6]. All these algorithms though work well but they suffer from some issues. Taking this into account a new category of evolutionary optimization algorithm has emerged. Firefly algorithm[7], Cuckoo search[8], Bat algorithm[9] and krill herd algorithm[10]. In this paper a comparative analysis of these recently introduced algorithms is presented in terms of area of application and its features. The paper is organized as follows: Section1 describes brief introduction. Section2 discusses firefly algorithm and its pseudo code. Section 3 and 4 describes cuckoo search and bat algorithm respectively. Section 5 discuss krill herd algorithm in detail. Section 6 gives a comparative study of different algorithms and techniques in tabular form. Finally the paper ends with conclusion and references in section 7 and 8 respectively. II. FIREFLY ALGORITHM Firefly algorithm[7][11][12][13] was introduced by X.S. Yang in 2008. Its inspiration is based on light emission, light absorption and mutual attractive behavior among fireflies. Initially, it was developed to solve optimization problems but later on it was used for solving discrete problems like travelling salesmen problem etc. Further it was also used in the fields of digital image processing and compression, clustering. The algorithm derives its inspiration from flashing behavior of fireflies. The three idealized rules in describing FA algorithm. 1. All fireflies will be attracted by each other without considering their sex. 2. The attractiveness of fireflies is directly proportional to the brightness of them, and both of them reduce with the increase in the distances between them. Normally a less bright one will move towards the brighter one. They will move randomly in case of same brightness. 3. In no case a less bright one can attract the brighter one then that and it moves randomly. The process of firefly algorithm starts with the initialization of the population of fireflies. Each firefly in a population represents a candidate solution. The size of the population determines the number of solutions or the size of the search space. In the next step, each firefly is evaluated based on their fitness (Light Intensity). Distance between any fireflies can be defined as a Cartesian distance. The distance function developed is used to find the distance between two fireflies. Attractiveness function is defined by using light intensity, distance and an absorption coefficient. Movement of firefly is defined by a movement function. It is defined by using current position, attractiveness and a random walk. A. Pseudo-code of FA Pseudo-code of firefly algorithm (FA) is given below. Begin 1) define Objective function; 2) generate an initial population of fireflies; 3) formulate light intensity associated with objective function; 4) define absorption coefficient 5) while (t<MaxGeneration) { for i=1:n (all n fireflies) for j=1:n (n fireflies) if (Ij> Ii), move firefly i towards j; end if vary attractiveness with distance ; evaluate new solutions and update light intensity; end for j end for i rank fireflies and find the current best; } 6) post-processing the results and visualization; End Fig. 01 Pseudo-code of FA [7][11][12][13] Prabhneet kaur et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5) , 2014, 6701-6704