A Hybrid Simplex Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization

Often, in many engineering applications it is required to find the best approximate solution of multi-objective optimization problems quick and with good accuracy. Multi-objective optimization problems are very common and important in real world applications, and as such many researchers are still working to devise various novel heuristic and mathematical approaches for their solution. Mathematical techniques were more common in the earlier times for optimal solution, but as many of the conventional and especially deterministic pure mathematical optimization schemes suffer from stagnation in local optima, there is a demand for new innovations for heuristic methods [1]. The history of heuristic algorithms as a revolutionary idea for stochastic optimization techniques goes back to 1984 [1,2]. Further, it is notable that stochastic optimization techniques are readily applicable to the real problem because of gradient free mechanism and local optima trap avoidance. Stochastic and multiobjective optimization is in use in different fields of study such as mechanical engineering [3], civil engineering [4], chemistry [5,6] and other [7]. There are also some heuristic algorithms suggested to solve challenging problems in power systems such as cuckoo search algorithm [8], Pseudo-Gradient based Particle Swarm Optimization with Constriction Factor (PG-PSOCF) [9] and Hybrid Particle Swarm Optimization (HPSO) [10].