Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
In this work single-objective, multi-objective and multi-parameter optimization models of a widely used modern machining process namely abrasive waterjet machining process are solved using a newly proposed optimization algorithm named Jaya algorithm. In order to solve the multi-objective optimization models, a posteriori version of Jaya algorithm named as “Multi-objective Jaya (MO-Jaya) algorithm” is used. Two optimization case studies of abrasive waterjet machining process are considered and the results of Jaya and MO-Jaya algorithms are found to be better than the results of well-known optimization algorithms such as simulated annealing (SA), particle swam optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, blackhole (BH) algorithm, bio-geography based optimization (BBO) algorithm, non-dominated sorting genetic algorithm (NSGA), non-dominated sorting teaching-learning-based optimization (NSTLBO) algorithm and sequential approximation optimization (SAQ). A set of Pareto-efficient solutions is obtained for each of theconsidered multi-objective optimization problems using MO-Jaya algorithm and the same is reported in this work. Hypervolume performance metric is used to compare the quality of the Pareto-front provided by MO-Jaya algorithm to the Pareto-front provided by NSGA and NSTLBO algorithms.