Orthogonal simulated annealing for multiobjective optimization

Water cycle algorithm for solving constrained multi-objective optimization problems

“…The value of X in relation (15) corresponds to a random number (uniformly distributed or determined from any appropriate distribution) between 0 and (C × d). Setting C > 1 allows streams to flow in different directions toward rivers.…”

“…Some of these methods include the strength Pareto evolutionary algorithm (SPEA) [5], SPEA2 [6], the Pareto archive evolution strategy (PAES) [7], the micro-genetic algorithm (micro-GA) [8], the non-dominated sorting genetic algorithm (NSGA) [9], NSGA-II [10], the multi-objective particle swarm optimization (MOPSO) [11], the Pareto dominant based multi-objective simulated annealing with self-stopping criterion (PDMOSA-I) [4], the vector immune algorithm (VIS) [12], the elitist-mutation multi-objective particle swarm optimization (EM-MOPSO) [13], the weight-based multi-objective immune algorithm (WBMOIA) [14], the orthogonal simulated annealing (OSA) [15], and the hybrid quantum immune algorithm (HQIA) [16].…”

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

“…Another novel enhancement in multi-objective simulated annealing has been proposed in Suman et al (2010). In their work, they build upon Suppapitnarm's SA (Suppapitnarm et al, 2000) by searching along directions based on orthogonal experimental design rather than mutating the solutions randomly.…”

Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

“…Thirdly, both SimE and SA have been applied to solve various multi-objective optimization problems. Some examples for SA are [25][26][27][28], and for SimE are [29][30][31]. Thus, the overall aim of this paper is to compare and study the performance of fuzzy SA and fuzzy SimE algorithms (with three optimization objectives) with respect to the existing SA and SimE approaches (with two objectives).…”