Hybrid Particle Swarm Optimization with a Modified Arithmetical Crossover for Solving Unconstrained Optimization Problems

In this article, we propose a new hybrid algorithm by combining the particle swarm optimization and genetic arithmetical crossover operator. We adjust the proposed algorithm in order to avoid the problem of stagnation and premature convergence of the population. Invoking the modified arithmetical crossover operator improves the exploration process of the proposed algorithm. We call the new proposed algorithm hybrid particle swarm optimization with a modified arithmetical crossover (HPSOAC). Also, we test HPSOAC on 26 functions (16 unconstrained optimization benchmark functions and 10 CEC05 special session functions). Furthermore, we compare the general performance of the proposed algorithm against 6 various particle swarm optimization algorithms. Moreover, we show the efficiency of the proposed algorithm and its ability to solve unconstrained optimization problems by giving several computational results.