A quantum based local search enhanced particle swarm optimization for binary spaces

Particle swarm optimization (pso) is a well-known swarm intelligencebased algorithm that simulates the movements of school or bird flocks in problem solving. although it is first introduced to solve unconstrained global optimization problems, there are numerous reported publications of pso involving various types of problems. however, as one can see from the related literature, compared to other types of implementations, discrete and binary pso applications are relatively fewer in number. in this context, in the present work, a 0-1 pso modification enhanced with a quantum-based local search procedure is developed. the mentioned quantum-based procedure generates randomly scattered particles referred to as quantum particles located within a sphere that is generated around the best-found solution by the algorithm. next, these particles are used for local search to achieve possible improvements on the best-found solution. the performance of the proposed approach is tested by using a 0-1 problem suite consisting of the commonly used one-max, deceptive, plateau and royal road functions. experimental study shows the effectiveness of the proposed approach in 0-1 problems.