Modified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update Abdul Hadi Hamdan #1 , Fazida Hanim Hashim #2 , Abdullah Zawawi Mohamed *3 , W. M. Diyana W. Zaki #4 , Aini Hussain #5 # Engineering Department of Electric, Electronic & System, Universiti Kebangsaan Malaysia, Bangi, Malaysia. 1 ab_hadi2329@yahoo.com 2 fazida@ukm.edu.my * Fujitsu Systems Global Solutions, Petaling Jaya, Malaysia. 3 abdullah.zawawi@fsgs.my.fujitsu.com Abstract— Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space, inspired by the social behaviour of some animal species. However, it has its limitations such as being trapped into a local optima and having a slow rate of convergence. In this paper, a new method of creating a combination of a developed Accelerated PSO and a new modulated inertia coefficient for the velocity update has been proposed. Random term based on particle neighbourhood has been added in the position update formula, inspired by the Artificial Bee Colony (ABC) algorithm. To verify the proposed modified PSO, experiments were conducted on several benchmark optimization problems. The results show that the proposed algorithm is superior in comparison with standard PSO and accelerated PSO algorithms. Keyword- Velocity Update, Global Best, Modulated Inertia, Particle Swarm Optimization I. INTRODUCTION Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space. It is an optimization method inspired by social behaviour of fish schooling and birds flocking and was defined by Kennedy and Eberhart in 1995 [1]. PSO is inspired by general artificial life and random search methods applied in evolutionary algorithm [2]. When travelling in a group, individual birds and fishes have the ability to move without colliding with each other. This is achieved by having each member follow its own group and adjust its position and velocity using the group information, thereby reducing the burden of individual’s effort in searching the target (food, shelter). Particle swarm optimization is quite similar to genetic algorithm because both are population-based and are equally effective [2]. The advantage of the PSO method lies in its lower complexity while having comparable performance as there are only a few parameters to be adjusted and manipulated. It also has better computational efficiency, need less memory space, and is less dependent on the CPU speed. Another advantage of PSO over derivative-based local search methods is that when solving a complicated optimization problem, the gradient information is not needed to perform the iterative search. In PSO, a member in the swarm is called a particle, representing a potential solution of a problem. A population of particles starts to move in a search space by following the current optimum particles and changing their positions in order to find out the optima. The position of a particle refers to a possible solution of the function to be optimized. Each particle has a fitness value, determined by evaluating a function using the particle’s position, and a velocity. The experiences of the swarm are used as a learning tool in the search for the global optima [3]. While it has been successfully used to solve many optimization tests and real-life optimization problems, the PSO method often suffers from premature convergence and getting trapped in a local optimum region. In order to achieve better algorithm performance, the original PSO algorithm has been modified by many researchers to be used in various types of applications. Shi et al. proposed an extended PSO based on inertia weight. A large inertia weight facilitates a global search while a small inertia weight facilitates a local search. By changing the inertia weight dynamically, the search capability is dynamically adjusted [4]. Nickabadi et al. proposed a new dynamic inertia weight PSO. While the former uses the fitness or iteration number as the basis ISSN (Print) : 2319-8613 ISSN (Online) : 0975-4024 Abdul Hadi Hamdan et al. / International Journal of Engineering and Technology (IJET) DOI: 10.21817/ijet/2016/v8i4/160804011 Vol 8 No 4 Aug-Sep 2016 1855