This paper presents a new multiobjective multicast routing algorithm (MMA) based on the Strength Pareto Evolutionary Algorithm (SPEA), which simultaneously optimizes the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. Sim...

A single-point local search method is presented as a simplification of the multiobjective tabu search. Some improvements are made to reach the Pareto front within small number of function evaluations. The performance of the proposed method is first verified by a small mathematical problem. It is shown that accurate Pareto optimal solutions with good diversity are obtained by using the proposed ...

Since the beginning of Nineties, research and application of multi-objective evolutionary algorithms (MOEAs) have found increasing attention. This is mainly due to the ability of evolutionary algorithms to find multiple Pareto-optimal solutions in one single simulation run. In this paper, we present an overview of the multi-objective evolutionary algorithms and then discuss a particular algorit...

When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a ...

We study in this paper the computation of Choquet optimal solutions in decision contexts involving multiple criteria or multiple agents. Choquet optimal solutions are solutions that optimize a Choquet integral, one of the most powerful tools in multicriteria decision making. We develop a new property that characterizes the Choquet optimal solutions. From this property, a general method to gener...

In this paper, evolutionary dynamic weighted aggregation methods are generalized to deal with three-objective optimization problems. Simulation results from two test problems show that the performance is quite satisfying. To take a closer look at the characteristics of the Pareto-optimal solutions in the parameter space, piecewise linear models are used to approximate the definition function in...

A problem that sometimes occurs in multiobjective optimization is the existence of a large set of Pareto-optimal solutions. Hence the decision making based on selecting a unique preferred solution becomes difficult. Considering models with rational B-efficiency relieves some of the burden from the decision maker by shrinking the solution set. This paper focuses on solving multiobjective optimiz...

This paper presents a new search procedure to tackle multi-objective traveling salesman problem (TSP). This procedure constructs the solution attractor for each of the objectives respectively. Each attractor contains the best solutions found for the corresponding objective. Then, these attractors are merged to find the Pareto-optimal solutions. The goal of this procedure is not only to generate...

One of the most challenging issues in multi-objective problems is finding Pareto optimal points. This paper describes an algorithm based on Benders Decomposition Algorithm (BDA) which tries to find Pareto solutions. For this aim, a multi-objective facility location allocation model is proposed. In this case, an integrated BDA and epsilon constraint method are proposed and it is shown that how P...

In this paper, we present PICPA, the “Population and Interval Constraint Propagation Algorithm” which is able to produce high quality approximate solutions while giving guaranteed bounds for the Pareto optimal front. These bounds allow us to know whether the heuristic solutions are close to or far away from the optimal front. PICPA combines “Interval Constraint Propagation” (ICP) techniques [1,...