A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems

Cat Swarm Optimization (CSO) is one of the new swarm intelligence algorith ms for finding the best global solution. Because of complexity, so metimes the pure CSO takes a long time to converge and cannot achieve the accurate solution. For solving this problem and improving the convergence accuracy level, we propose a new improved CSO namely 'Adaptive Dynamic Cat Swarm Optimization'. First, we add a new adaptive inertia weight to velocity equation and then use an adaptive acceleration coefficient. Second, by using the information of t wo prev ious/next dimensions and applying a new factor, we reach to a new position update equation composing the average of position and velocity informat ion. Experimental results for six test functions show that in co mparison with the pure CSO, the proposed CSO can takes a less time to converge and can find the best solution in less iteration.