Modified Position Update in Spider Monkey Optimization Algorithm

Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems. Nature inspired meta-heuristics has turn out to be an intimidating and out of the ordinary field of research in the midst of researchers who are trying to solve complex optimization problems. More or less all meta-heuristics make use of both randomization and local search. Due to randomization it can be in motion away from local search to global search space. That's why meta-heuristics are unsurpassed more suitable for global optimization problems. Meta heuristic algorithms have two major components: diversification and intensification. Diversification is the process of exploration of the large search space and ensures that solution does not ensnare in local optima at the same time as intensification concentrates on best solution for convergence to optimality (1). Population based meta-heuristics do not give assurance for the optimal solution but they provide near- optimal solution for most difficult optimization problems. Researchers have evaluated this type of behaviors and developed strategies with the intention of can be used to solve nonlinear and discrete optimization problems. Preceding research (2, 3, 4, and 5) in last decade has shown that strategies based on swarm intelligence have enormous prospective to come across solutions of real world optimization problems. The algorithms that have emerged in topical years consist of ant colony optimization (ACO) (2), particle swarm optimization (PSO) (3), bacterial foraging optimization (BFO) (6), Artificial bee colony (ABC) optimization algorithm established by D. Karaboga (7) and most recently developed Spider Monkey Optimization (SMO) algorithm (10)is new entry in class of swarm intelligence. This SMO algorithm is inspired by fission fusion social structure (FFSS) based foraging behavior of spider monkeys when searching for quality food source and for mating. Similar to any other population based optimization techniques, ABC consists of a population of inherent solutions. The inherent solutions are food sources of honey bees. The fitness is decided in terms of the quality of the food source that is nectar amount. ABC is relatively a straightforward, speedy and population based stochastic search technique in the field of nature inspired algorithms. SMO is also similar to ABC in nature. There are two fundamental processes which drive the swarm to update in ABC: the deviation process, which enables exploring different fields of the search space, and the selection process, which ensures the exploitation of the previous experience. However, it has been shown that the ABC may occasionally stop moving toward the global optimum even though the population has not encounter to a local optimum (8). It can be observed that the solution search equation of ABC algorithm is good at exploration but poor at exploitation (9). Therefore, to maintain the proper balance between exploration and exploitation behavior of ABC, it is highly expected to develop a local search approach in the basic ABC to intensify the search region.