A Hybrid Algorithm to Solve Multi-model Optimization Problems Based on the Particle Swarm Optimization with a Modified Firefly Algorithm

摘要

Multi-model optimization brings out the concept of finding all or most of the multiple solutions of a problem, as opposed to a single best solution. Recently many researches have focused on finding the capability of swarm intelligence and evolutionary algorithms in solving such mathematical problems. This paper proposes a hybrid algorithm combining the particle swarm optimization algorithm (PSO) with a new modified firefly algorithm, which has been originally modified for the root finding purpose of nonlinear systems of equations (MODFA) to be used in multi-model optimization. The concept of the hybrid algorithm divides the solution into two parts where PSO is used to find one global optimum and with that the MODFA finds other optimal solutions as much as possible. Benchmark multi-model optimization problems with different dimensions have been used on the new algorithm to test its capability. Results obtained for the evaluation criteria demonstrate the suitability of the method. The results compared with several state-of-the-art multi-model optimization algorithms showed that the proposed hybrid algorithm performs competitively with these algorithms.