The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. The performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. The operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDP. Finally, TOPSIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision-maker's preference.

This paper proposes a newly meta-heuristic approach, steady-state sine cosine genetic algorithm-based chaotic search, for solving nonlinear programming and engineering applications. It is a combination of sine cosine approach (SCA), steady-state genetic algorithm (SSGA), and chaotic search (CS), and named as chaos-enhanced SCA with SSGA. The proposed approach integrates SSGA’s exploitation ability and SCA’s exploration ability and local search capability of CS. The performance of the new approach works in two different stages. Firstly, SCA and SSGA start together to increase exploration capability and exploitation tendencies. Secondly, CS used to improve the approximate solution obtained from the first stage and reach the global solution. Hence, the proposed new approach will be more robust as it avoids trapping into local minima in addition to the speed of the search process and rapid convergence towards the global solution. The efficiency of the proposed approach is verified by using it to solve 32 well-known benchmark problems and different engineering design problems. Simulation results show that the proposed approach is competitive and better in most cases as a comparison to others.

This paper presents genetic algorithm based on K-means clustering technique for solving multi-objective resource allocation problem (MORAP). By using k-means clustering technique, population can be divided into a specific number of subpopulations with dynamic size. In this way, different GA operators (crossover and mutation) can be applied to each subpopulation instead of one GA operators applied to the whole population. The purpose of implementing K-means clustering technique is preserving and introducing diversity. Also it enable the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other. Two test problems taken from the literature are used to compare the performance of the proposed approach with the competing algorithms. The results have been demonstrated the superiority of the proposed algorithm and its capability to solve MORAP.