Search space reduction of particle swarm optimization for hierarchical similarity measurement model

Particle Swarm Optimization (PSO) is one of many optimization techniques used to find a solution in many areas not limited to engineering or mathematics. It can discover the solution to a problem of finding input to a program based on the similarity of program's execution. However, identifying such solutions with standard PSO is not very efficient or in a few cases, not possible. There is a high probability that particles are stuck in an area of local maxima. The main reason is due to excessive exploitation steps. In addition, when the new exploration starts, there is no guarantee that particles will no longer be generated from earlier explored areas. This paper presents an algorithm of Search Space Reduction (SSR) applied to PSO for Hierarchical Similarity Measurement (HSM) model of program execution. The algorithm uses a fitness function computed from HSM model. SSR helps to find the solution by eliminating areas where the solution is most likely not to be found. It improves the optimization process by reducing the excessive exploitation step. Moreover, SSR can be applied to all variants of PSO. The experimental results demonstrate that PSO with SSR is the most effective method among all other three techniques used in experiment. SSR increases effectiveness in finding a solution by 73%. For each program under the experiment, SSR algorithm was able to find all solutions with the smallest number of exploitations. Regardless of the program's complexity, PSO with SSR usually manipulates the searching process faster than both versions of PSO without SSR.