Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblem...

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...

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...

Multiobjective selection operators are a popular and straightforward tool for preserving diversity in evolutionary optimization algorithms. One application area where diversity is essential is multimodal optimization with its goal of finding a diverse set of either globally or locally optimal solutions of a single-objective problem. We therefore investigate multiobjective selection methods that...

Many real-life problems have a natural representation in the framework of multiobjective optimization. Evolutionary algorithms are generally considered one of the most successful methods for solving the multiobjective optimization problems. In this paper we present state-of-the-art multiobjective evolutionary algorithms and briefly discuss their advantages and disadvantages. In the last section...

The $-calculus process algebra for problem solving applies the cost performance measures to converge to optimal solutions with minimal problem solving costs. The same meta-level kΩ-optimization control can be used to find the best quality solutions (expressed as optimization problems), the most effective solutions (expressed as search optimization problems), or to find solutions representing th...

Multi objective optimization is a promising field which is increasingly being encountered in many areas worldwide. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used to solve Multi objective problems. Various multiobjective evolutionary algorithms have been devel...

The development of electricity networks towards the future smart grids is naturally accompanied by increasing complexity of technical, economic and environmental problems. The new challenges require the development of new techniques and optimization methods, including specific approaches to multiobjective optimization problems. This paper focuses on basic and multiobjective optimization methods...

In this chapter, we present a survey of constraint-handling techniques based on evolutionary multiobjective optimization concepts. We present some basic definitions required to make this chapter self-contained, and then we introduce the way in which a global (single-objective) nonlinear optimization problem is transformed into an unconstrained multiobjective optimization problem. A taxonomy of ...