In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in ¯exible manufacturing systems. The PSGA is used to generate approximately ef®cient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the ®rst implementation of PSGA to solve a multiobjective optimization problem (MOP)....

This paper deals with multiobjective optimization in the context of multiattribute utility theory. The alternatives (feasible solutions) are seen as elements of a product set of attributes and preferences over solutions are represented by generalized additive decomposable (GAI) utility functions modeling individual preferences or criteria. Due to decomposability, utility vectors attached to sol...

A self-adaptive Pareto Evolutionary Multiobjective Optimization (EMO) algorithm based on differential evolution is proposed for evolving locomotion controllers in an artificially embodied legged creature. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incu...

 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuou...

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis of selective withdrawal from a thermally stratified reservoir. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated (Pareto) solutions when minimizing deviations from outflow water quality targets of: (i) temperature; (ii) dissolved oxygen (DO); (iii) t...

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorpora...

Conflict resolution, e.g. negotiation, is frequently about an interactive process that forces agents to make concessions in order to resolve the conflict. In multilateral negotiations, concessions might be directed to one or another partner. In isolated negotiations such directed concessions might be less useful, but may become important for interdependent negotiations. We present weight-based ...

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these p...

A new algorithm is proposed to solve constrained multi-objective problems in this paper. The constraints of the MOPs are taken account of in determining Pareto dominance. As a result, the feasibility of solutions is not an issue. At the same time, it takes advantage of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. The output ...

This work presents a new learning scheme for improving generalization of Multilayer Perceptrons (MLPs). The proposed Multi-objective algorithm (MOBJ) approach minimizes both the sum of squared error and the norm of network weight vectors to obtain the Pareto-optimal solutions [1]. Preliminar results are shown in [3]. Since the Pareto-optimal solutions are not unique, we need a decision phase in...