A new Pareto front approximation method is proposed for multiobjective optimization problems with bound constraints. The method employs a hybrid optimization approach using two derivative free direct search techniques, and intends to solve blackbox simulation based multiobjective optimization problems where the analytical form of the objectives is not known and/or the evaluation of the objectiv...

In this article, a fuzzy goal programming (GP) and method of approximation is presented for the solution of a multiobjective linear plus linear fractional programming problem. In the proposed approach, membership functions are defined for each fuzzy goal and then a method of variable change on the underand overdeviational variables of the membership functions associated with the fuzzy goals of ...

In this paper we are concerned with finding the Pareto optimal front or a good approximation to it. Since non-dominated solutions represent the goal in multiobjective optimisation, the dominance relation is frequently used to establish preference between solutions during the search. Recently, relaxed forms of the dominance relation have been proposed in the literature for improving the performa...

Feature selection is a common and key problem in many classification and regression tasks. It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it involves feature subset size minimisation and performance maximisation. This paper presents a multiobjective evolutionary approach for feature selection. A novel commonality-based crossover operator is introduced an...

This paper models the process of a recommender system as a multiobjective optimization problem, a discrete particle swarm optimization framework is established and has been integrated into multiobjective optimization, consequently, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the modeled optimization problem. Each run of the current mainstream recommender...

This paper fundamentally investigates the performance of evolutionary multiobjective optimization (EMO) algorithms for computationally hard 0–1 combinatorial optimization, where a strict theoretical analysis is generally out of reach due to the high complexity of the underlying problem. Based on the examination of problem features from a multiobjective perspective, we improve the understanding ...

This paper presents a fuzzy goal programming (GP) procedure for solving a multiobjective linear plus linear fractional programming problem. In the proposed approach GP model for achievement of the highest membership value of each of fuzzy goal defined for the linear plus linear fractional objectives is formulated. In the solution process, the method of variable change on the underand overdeviat...

In this work, a new approach to selection in multiobjective evolutionary algorithms (MOEAs) is proposed. It is based on the portfolio selection problem, which is well known in financial management. The idea of optimizing a portfolio of investments according to both expected return and risk is transferred to evolutionary selection, and fitness assignment is reinterpreted as the allocation of cap...

Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation t...

This paper presents a new method for multiobjective optimisation based on gradient projection and local region search. The gradient projection is conducted through the identi®cation of normal vectors of an ecient frontier. The projection of the gradient of a nonlinear utility function onto the tangent plane of the ecient frontier at a given ef®cient solution leads to the de®nition of a feasib...