A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic algorithm optimization is utilized to gain the unique benefits of each optimization algorithm. Therefore, the proposed hybrid algorithm makes use of the functions and operations of both algorithms such as mutation, traditional or classical crossover, multiple-crossover and the PSO formula. Selection of these operators is based on a fuzzy probability. The performance of the hybrid algorithm in the case of solving both single-objective and multi-objective optimization problems is evaluated by utilizing challenging prominent benchmark problems including FON, ZDT1, ZDT2, ZDT3, Sphere, Schwefel 2.22, Schwefel 1.2, Rosenbrock, Noise, Step, Rastrigin, Griewank, Ackley and especially the design of the parameters of linear feedback control for a parallel-double-inverted pendulum system which is a complicated, nonlinear and unstable system. Obtained numerical results in comparison to the outcomes of other optimization algorithms in the literature demonstrate the M. Andalib Sahnehsaraei Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, Rasht, Iran e-mail: [email protected] M.J. Mahmoodabadi S.M. Mortazavi Yazdi Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran e-mail: [email protected] S.M. Mortazavi Yazdi e-mail: [email protected] M. Taherkhorsandi (&) K.K. Castillo-Villar Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA e-mail: [email protected]; [email protected] K.K. Castillo-Villar e-mail: [email protected] © Springer International Publishing Switzerland 2015 Q. Zhu and A.T. Azar (eds.), Complex System Modelling and Control Through Intelligent Soft Computations, Studies in Fuzziness and Soft Computing 319, DOI 10.1007/978-3-319-12883-2_2 45 efficiency of the hybrid of particle swarm optimization and genetic algorithm optimization with regard to addressing both single-objective and multi-objective optimization problems.