A Novel Hybrid Genetic Algorithm with Weighted Crossover and Modified Particle Swarm Optimization

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although these methods are approximate methods (i.e. their solutions are good, but probably not optimal), they do not require the derivatives of the objective function and constraints. Also, the heuristics use probabilistic transition rules instead of deterministic rules. Here, an evolutionary algorithm based on the hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), denoted by HGAPSO, is developed. Particle Swarm Optimization (PSO) is a very popular optimization technique, but it suffers from a major drawback of a possible premature convergence i.e. convergence to a local optimum and not to the global optimum. This paper attempts to improve on the reliability of PSO by addressing the drawback. This modified method would free PSO from local optimum solutions; enable it to progress towards the global optimum searching over wider area. So the probability, of not getting trapped into local optima gets enhanced which gives better assurance to the achieved solution. Experiments shows that the proposed method will provide better solution.