Multiobjective Dynamic Programming (MODP) is a general problem solving method used to determine the set of Pareto-optimal solutions in optimization problems involving discrete decision variables and multiple objectives. It applies to combinatorial problems in which Pareto-optimality of a solution extends to all its sub-solutions (Bellman principle). In this paper we focus on the determination o...

There is an increasing trend in the use of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems of the allocation of water resources. However, typically the outcome is a set of Pareto optimal solutions which make up a trade-off surface between the objective functions. For decision makers to choose a satisfactory alternative from a set of Pareto-optimal ...

It is demonstrated that multiple objective decision theory provides a suitable formalism to encompass ideas from behavior-based system synthesis and control, where each behavior is cast as an objective function estimator. Action selection is comprised of generating and then selecting a set of satisscing solutions among a set of solutions that are Pareto-optimal. The basic ideas of the proposed ...

In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this pr...

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...

This paper presents a multi-objective optimal power flow technique using particle swarm optimization. Two conflicting objectives, generation cost, and environmental pollution are minimized simultaneously. A multiobjective particle swarm optimization method is used to solve this highly nonlinear and non-convex optimization problem. A diversity preserving technique is incorporated to generate eve...

Many real-world optimization problems are evaluated in terms of multiple, often conflicting criteria or objective functions. When there is no a priori information about the importance of each objective, the solutions to such a multi-objective optimization (MOO) problem are usually compared in terms of Pareto dominance [1, 2]: A solution dominates another one if the former is not worse than the ...

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional mul...

In this paper a new MultiOBjective Evolution Strategy (MOBES) for solving multi-objective optimization problems subject to linear and nonlinear constraints is presented. MOBES is based on the new concept of C-, F-and N-tness, which allows systematically to handle constraints and (in)feasible individuals. The existence of niche infeasible individuals in every population enables to explore new ar...

How to conduct automated business negotiation over the Internet is an important issue for agent research. In most bilateral negotiation models, two negotiation agents negotiate by sending proposals and counterproposals back and forth. Proposals and counterproposals usually use a set of negotiation primitives to define the contents. These primitives deal with complete proposals or the whole nego...