SMPSO: A new PSO-based metaheuristic for multi-objective optimization

Abstract

In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the nondominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

Dedicated system for design, analysis and optimization of AC-DC converters

“…An efficient implementation of such an algorithm should consider the analysis to select optimal parameters for elitism, crossover, population size, and number of generations best suited for a given application. In this work the nondominated sorted genetic algorithm III (NSGAIII), strength Pareto evolutionary algorithm 2 (SPEA2) and particle swarm optimization algorithms (eMOEA, OMOPSO, SMOPSO) are analysed [29][30].…”

Particle Swarm Optimization of Antenna Arrays With Efficiency Constraints

“…The algorithm does not implicitly exclude non-feasible solutions from the archive; it prioritizes archiving of feasible solutions over the non-feasible ones, taking also under consideration the respective violation of constraints. In this work, the speed-constrained SMPSO algorithm is used, that employs an appropriate constriction coefficient to limit the particles velocity [18]. The exploratory ability of the algorithm is enhanced by allowing each particle to mutate with probability p m using a polynomial probability distribution controlled by a positive index parameter η m [19].…”

“…The optimization parameters used are those suggested by Nebro et al in [18]; individuality c 1 and sociality c 2 were allowed to take values in the range 1.5 to 2.5, while the archive and swarm population size were set equal to 50. The mutation probability p m was chosen as the inverse of the problem dimensionality and a distribution index value η m of 60 was set to enhance the algorithm's search capabilities.…”

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

“…For MOPs, the effectiveness of non-dominated sorting has long been recognized and most of the existing MOEAs adopted this strategy, e.g., NSGA-II [7], PESA-II [9], GDE3 [15], SMPSO [16], EAG-MOEA/D [17], MOEA/IGD-NS [18], etc. In the existing MOEAs, non-dominated sorting is mainly performed in environmental selection, where solutions are divided into several ranks by non-dominated sorting and those in the same rank are distinguished by additional criteria, such as crowding distance in NSGA-II, GDE3, SMPSO, and EAG-MOEA/D, the region-based metric in PESA-II, and the enhanced IGD in MOEA/IGD-NS.…”