Study of self-stopping PDMOSA and performance measure in multiobjective optimization

The paper presents improvements in simulated annealing based multiobjective algorithms and the modifications in the metrics methods used for the performance measure in multiobjective optimization. Simulated annealing (SA) based multiobjective algorithms have been made self-stopping as they do not require a total number of iterations. The performance metrics have been improved such that they can be used for the performance measure of a multiobjective algorithm and two multiobjective algorithms without a known true Pareto set. Pareto dominant based multiobjective simulated annealing with self-stopping criterion (PDMOSA-I) has been compared with existing Suppapitnarm multiobjective simulated annealing (SMOSA) in detail. The computational cost with PDMOSA-I is less than that with SMOSA. With the help of metrics methods, it has been found that the quality, extent and diversification of a Pareto set of solutions produced by PDMOSA-I is better than that produced by SMOSA.