The optimization of multiple conflictive objectives at the same time is a hard problem. In most cases, a uniform distribution of solutions on the Pareto front is the main objective. We propose a novel evolutionary multi-objective algorithm that is based on the selection with regard to equidistant lines in the objective space. The so-called rakes can be computed efficiently in high dimensional o...

This paper studies an evolutionary algorithm to solve a new multiobjective optimization problem, the Pickup and Delivery Problem with Time Windows and Demands (PDPTW-D), which extends PDP and PDP-TW. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles in order to serve given transportation requests. The proposed algorithm ...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

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...

This paper addresses the problem of controlling mutation strength in multi-objective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimization problems. In this study we examine whether...

1 Abstract Multi-objective optimization addresses problems with several design objectives, which are often conflicting, placing different demands on the design variables. In contradiction to traditional optimization methods, which combine all objectives into a single figure of merit, parallel optimization strategies such as evolutionary algorithms allow direct convergence to the Pareto front. T...

Owing to the incremental demands for electrical energy, distributed generation (DG) sources are becoming more important in distribution systems. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel Strength Pareto Evolutionary Algorithm (SPEA) is represented for optimal location and sizing of DG on distribution sy...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean-variance model in which the variance is replaced with an industry standard risk measure, Value-atRisk (VaR), in order to better assess market risk exposure associated with financia...

Multiobjective optimization problems (MOPs) have attracted intensive efforts from AI community and many multiobjective evolutionary algorithms (MOEAs) were proposed to tackle MOPs. In addition, a few researchers exploited MOEAs to solve constraint optimization problems (COPs). In this paper, we investigate how to tackle a MOP by iteratively solving a series of COPs and propose the algorithm nam...

Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (μ+1)-algorithm. The convergence of the algorithm is defined, and the general convergence condition...