Multi-objective social group optimization for machining process

A multi-objective social group optimization (MOSGO) is proposed in this study as a method for resolving problems with multiple objectives. The initial step is to use an external chronicle with a predetermined size to store the existing non-dominated Pareto optimum solutions. Following that, solutions are chosen from this repository using a roulette wheel mechanism based on the coverage of solutions to guide individuals in the direction of promising regions of multi-objective search spaces. The proposed method is tested on ten CEC2009 multi-objective benchmark problems and it has been compared to two well-known meta-heuristics: the multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm. It is essential to the machining processes to choose the optimum machining parameters in order to guarantee product quality, lower machining costs, boost productivity, and preserve resources for sustainability. Hence, MOSGO, a posterior multi-objective optimization method, is used in this study to tackle the multi-objective optimization problems of three machining processes, including turning, wire-electric-discharge machining, and laser cutting. The results of the MOSGO algorithm are compared with the results obtained using GA, NSGA-II, PSO, and iterative search methods and are found to be comparable. Here it has been observed that the MOSGO algorithm achieved the Pareto-optimal set of solutions in a very less number of function evaluations as compared to other algorithms showing a higher convergence speed. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal sets of solutions are helpful for real production systems and will aid the decision-maker in tumultuous situations.