In this paper, a fuzzy multi-objective constrained optimization problem (FMCOP) is converted to a single-objective constrained optimization problem with equality and inequality constraints (SCOP) by using α−level set of the fuzzy vector and weighting approach. A trust-region algorithm for solving problem (SCOP) is introduced to obtain α−pareto optimal solutions of problem (FMCOP). An εα−stabili...

Evolutionary algorithms EAs have become es tablished as the method at hand to explore the Pareto optimal front in multiobjective optimiza tion problems This is not only because there are hardly any alternatives in the eld of multiob jective optimization due to their inherent paral lelism and their capability to exploit similarities of solutions by crossover they are able to capture several Pare...

In this paper, artificial neural networks for solving multiobjective optimization problems have been considered. The Tank-Hopfield model for linear programming has been extended, and then the neural model for finding Pareto-optimal solutions in the linear multi-criterion optimization problem with continuous decision variables has been discussed. Furthermore, the model for solving quasi-quadrati...

Aiming at the multi-objective essence of optimal audio watermarking problem, we propose a novel audio watermarking framework in this paper, which can optimally balance all conflicting objectives of the problem, fidelity and robustness against different attacks. In the proposed framework, a multi-objective particle swarm optimization technique based on fitness sharing is applied to search optima...

another, so that it is not possible to improve on any of the objective functions without deteriorating at least one of the other objective functions. This is known as the concept of Pareto optimality (Rao, 1991). In multiobjective optimization, as opposed to single-objective optimization, there may not exist an unambigous optimal solution (global minimum or global maximum). Characteristic of th...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the true Paretooptimal solutions with a widely spread distribution of solut...

This paper presents a Pareto-based bi-objective optimization of hazardous materials vehicle routing and scheduling problem with time windows and shows its application to a realistic hazardous material logistics instance. A meta-heuristic solution algorithm is also proposed, which returns a set of routing solutions that approximate the frontier of the Pareto optimal solutions based on total sche...

Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modelled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can ...

This paper proposes a novel approach to generate a uniform distribution of the optimal solutions along the Pareto frontier. We make use of a standard mathematical technique for optimization namely line search and adapt it so that it will be able to generate a set of solutions uniform distributed along the Pareto front. To validate the method, numerical bi-criteria examples are considered. The m...

Many studies are performed by researchers about Shell and Tube Heat Exchanger but the Multi-Objective Big Bang-Big Crunch algorithm (MOBBA) technique has never been used in such studies. This paper presents application of Thermal-Economic Multi-Objective Optimization of Shell and Tube Heat Exchanger Using MOBBA. For optimal design of a shell and tube heat exchanger, it was first thermally model...