International Journal of Information Technology (IJIT) – Volume 3 Issue 2, Mar - Apr 2017 ISSN: 2454-5414 www.ijitjournal.org Page 49 A Review on Glowworm Swarm Optimization T. Kalaiselvi [1] , P. Nagaraja [2] , Z. Abdul Basith [3] Department of Computer Science and Applications, The Gandhigram Rural Institute-Deemed University, Gandhigram, Dindigul. Tamil Nadu – India ABSTRACT This paper presents a review on glowworm swarm optimization (GSO) algorithm based methods. GSO is a current nature- inspired optimization algorithm that simulates the behavior of the lighting worms. GSO algorithm is suitable for a concurrent search of several solutions and dissimilar or equal objective function values. A number of reviews are provided that describe applications of GSO algorithms in different domains, such as clustering and various optimization problems. Keywords — Clustering, Optimization, Swarm Intelligence, Glowworm Swarm Optimization. I. INTRODUCTION The behaviour of a solitary ant, bee, termite and wasp often is too simple, but their combined and social actions are of paramount consequence. The collective and social behaviour of living creatures are motivated the researchers to undertake the lessons of today what is known as Swarm Intelligence (SI). Historically, the phrase SI was coined by Beny and Wang in the context of cellular robotics [1]. A group of researchers in different parts of the world currently works almost at the same time to study the versatile behavior of different living creatures and in particular the social insects. The efforts to mimic such behaviors through computer imitation finally resulted into the fascinating field of SI. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents must behave, limited interactions between such agents often lead to the emergence of global behavior. A lot of biological creatures such as fish schools and bird flocks clearly display structural order, with the behavior of the organisms so integrated that even though they may change shape and direction, they appear to move as a single coherent entity [2]. The main properties of the collective behavior can be pointed out as follows and is summarized in Figure 1. The homogeneity is every bird in flock has the same behavioral model. The flock moves without a leader, even though temporary leaders seem to appear. The locality is nearest flock-mates just influence the motion of each bird. Vision is considered to be the most important senses for flock organization. The collision avoidance is used to avoid colliding with nearby flock mates. The velocity matching is attempted to match velocity with nearby flock mates. The flock centring is attempt to stay close to nearby flock mates Individuals attempt to maintain a minimum distance between themselves and others at all times. This rule is given the highest priority and corresponds to a frequently observed behavior of animals in nature [3]. If individuals are not performing an avoidance maneuver they tend to be attracted towards other individuals (to avoid being isolated) and to align themselves with neighbors [4], [5]. II. GLOWWORM SWARM OPTIMIZATION The Glowworm Swarm Optimization (GSO) is a original swarm intelligence algorithm for optimization developed by Krishnanand and Ghose which imitate the flashing behaviour of glowworms [6]. Each glowworm carries a luminescence amount called luciferin, which is decided by the function value of glowworm’s current location. All through the course of movement, glowworm identifies its neighbors based on local-decision area and selects a neighbor which has a luciferin value higher than its own using a probabilistic mechanism and moves on the way to it [7–12]. The GSO approach has been compared to the complete search algorithm, Fig. 1 Major character of collective behaviour RESEARCH ARTICLE OPEN ACCESS