A multi-objective evolutionary algorithm (MOEA) approach is presented in this paper. The algorithm (DFBMOEA) aims to improve convergence of Paretobased MOEAs to the true Pareto optimal set/Pareto front and remove decision maker interaction from the process. A novel distance function is used as a fitness function for MOEA. A range equalisation function and a reference vector are utilised to elim...

A new Pareto front approximation method is proposed for multiobjective optimization problems with bound constraints. The method employs a hybrid optimization approach using two derivative free direct search techniques, and intends to solve blackbox simulation based multiobjective optimization problems where the analytical form of the objectives is not known and/or the evaluation of the objectiv...

Many real-world optimization problems involve several conflicting objectives that must be optimized simultaneously. Furthermore, most optimization problems have a dynamic structure and change over time. In addition to trying to establish trade-offs among conflicting objectives and explore a diverse set of solutions on a Pareto-optimal front, a dynamic multi-objective optimization (DMOO) algorit...

The automated shape optimization of an electrostatic micromotor with radial field is tackled. Two objectives in mutual contrast i.e. static torque and torque ripple, depending on two design variables, are considered. An innovative procedure for vector optimization which aims at obtaining as many optimal solutions as possible, is presented. To this end, a non-dominated sorting genetic algorithm ...

In this paper we present a new image thresholding method based on a multiobjective Genetic Algorithm using the Pareto optimality approach. We aim to optimize multiple criteria in order to increase the segmentation quality. Thus, we’ve adapted the well known Non Domination Sorting Genetic Algorithm for this purpose so that it takes into consideration the contribution of the objective functions i...

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, c...

This paper presents a modified Differential Evolution (DE) algorithm called OCMODE for solving multi-objective optimization problems. First, the initialization phase is improved by using the opposition based learning. Further, a time varying scale factor F employing chaotic sequence is used which helps to get a well distributed Pareto front by the help of non dominated and crowding distance sor...

Multi-objective genetic algorithm (GAs) is used for pump design pareto optimization, competing objectives for centrifugal pump design are total head (H), input power (Ps), hydraulic efficiency ( H η ), and input parameter are capacity (Q), and the outer radius of the impeller ( ). Multi-objective presents a set of compromised solution, and provides non-dominated optimal choices for designer. 2 ...