Evaluation of Combined Pareto Multiobjective Differential Evolution on Tuneable Problems

Abstract

Many optimization problems in engineering involve the satisfaction of multiple objectives within the limits of certain constraints. Methods of evolutionary multi-objective algorithms (EMOAs) have been proposed and applied to solve such problems. Recently, a combined Pareto multi-objective differential evolution (CPMDE) algorithm was proposed. The algorithm combines Pareto selection procedures for multi-objective differential evolution to implement a novel selection scheme. The ability of CPMDE in solving unconstrained, constrained and real optimization problems was demonstrated and competitive results obtained from the application of CPMDE suggest that it is a good alternative for solving multi-objective optimization problems. In this work, CPMDE is further tested using tuneable multi-objective test problems and applied to solve a real world engineering design problem. Results obtained herein further corroborate the efficacy of CPMDE in multi-objective optimization.

<b>Entropy-Based weighting applied to normal boundary intersection approach: the vertical turning of martensitic gray cast iron piston rings case

“…The authors aimed to optimize, with the application of DOE, a process of vertical turning to determine the condition that led to a maximum life of the cutting tool (mm), high productivity (parts hour -1 ), and minimum cost (US$ part -1 ). Using feed rate (mm rev -1 ) and rotation (rpm) of the cutting tool as the decision variables, a full factorial design 2 2 (2 factors and 2 levels) was performed, with 4 axial points and 5 center points, as suggested by Box and Draper (1987), generating 13 experiments (Table 2). The criteria for the end of tool life was based on the maximum flank wear (= 0.3mm), the breakdown of the tool, and the chipping of the piston ring (SEVERINO et al, 2012).…”

“…In practical situations, however, solving a given problem usually calls for the systematic and simultaneous analysis of more than one objective function, resulting in multiobjective optimization (HUANG et al, 2006, ADEYEMO;OLOFINTOYE, 2014).…”

Optimised recrystallisation model using multiobjective evolutionary and genetic algorithms and k-optimality approach

“…The remedy is to use some alternate less rigid optimal conditions. [19][20][21] One such option is to use the k-optimality condition recently proposed by Farina and Amato. 22 The conditions implemented here allow a limited amount of inferiority in some of the objectives.…”