Research into solving multiobjective optimization problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Paretobased Evolutionary Algori...

Multi-objective-optimization-based positioning of houses in a residential neighborhood is described. The task is the placement of the buildings in a favorable configuration constrained by two objectives, which are garden performance and visual privacy performance requirements. The method used is evolutionary computation with the Pareto front based on a weighted function of the objectives. It is...

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...

In this paper, we propose a sufficient condition for a solution to be optimal for a 2-additive Choquet integral in the context of multiobjective combinatorial optimization problems. A 2-additive Choquet optimal solution is a solution that optimizes at least one set of parameters of the 2-additive Choquet integral. We also present a method to generate 2-additive Choquet optimal solutions of mult...

Many real-world problems show both multiobjective as well as dynamic characteristics. In order to use multiobjective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the behavior of these algorithms in dynamic environments is necessary. Dynamic fitness functions can be classified into problems with moving Pareto fronts and Pareto sets having varying speed, shape...

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...

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the two algorithms is carried out in order to improve the performance of th...

Pareto efficiency is a seminal condition in the bargaining problem which leads autonomous agents to a Nash-equilibrium. This paper investigates the problem of the generating Pareto-optimal offers in bilateral multi-issues negotiation where an agent has incomplete information and the other one has perfect information. To this end, at first, the bilateral negotiation is modeled by split the pie g...

The buffer allocation problem (BAP) has been widely studied by researchers while pursuing diverse research goals. Similarly, the cross-entropy method has been applied to a variety of optimisation problems with single objectives. In this article it is extended to the multiobjective case and proposed as a computationally economic approach to optimise at least two conflicting objectives of the BAP...

Real-world optimization problems are often subject to uncertainties, which can arise regarding stochastic model parameters, objective functions and decision variables. These uncertainties can take different forms in terms of distribution, bound and central tendency. In the multiobjective context, several studies have been proposed to take uncertainty into account, and most of them propose an ex...