Abstract Evolutionary algorithms have been widely used to tackle multiobjective optimization problems. Incorporating preference information into the search of evolutionary algorithms for multi-objective optimization is of great importance as it allows one to focus on interesting regions in the objective space. Zitzler et al. have shown how to use a weight distribution function on the objective ...

Multi-Objective Evolutionary Algorithms (MOEAs) have been proved efficient to deal with Multi-objective Optimization Problems (MOPs). Until now tens of MOEAs have been proposed. The unified mode would provide a more systematic approach to build new MOEAs. Here a new model is proposed which includes two sub-models based on two classes of different schemas of MOEAs. According to the new model, so...

In this paper, we review some of the most popular evolutionary algorithms and a systematic comparison among them. Then we show its importance in single and multi-objective optimization problems. Thereafter focuses some of the multi-objective evolutionary algorithms, which are currently being used by many researchers, and merits & demerits of multi-objective evolutionary algorithms (MOEAs). Fina...

in financial matters, portfolio can be interpreted as a combination or a series of investments hold by an institution or a person. portfolio optimization is one of the most important concerns of investors for maximizing the portfolio in financial markets. the formation of portfolio is a vital and critical decision for the companies.  in fact, the selection of portfolio is to specify the capital...

This chapter provides the basic concepts necessary to understand the rest of this book. The introductory material provided here includes some basic mathematical definitions related to multi-objective optimization, a brief description of the most representative multi-objective evolutionary algorithms in current use and some of the most representative work on performance measures used to validate...

ix Zusammenfassung xi Statement of Contributions xiii Acknowledgments xv List of Symbols and Abbreviations xv  Introduction  . Multi-objective Optimization . . . . . . . . . . . . . . . . . . . . .  . Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . .  . Research Questions . . . . . . . . . . . . . . . . . . . . . . . . .  . Contributions and Overview . . . . . ....

In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and control for multi-objective optimal control problems (MOOCP)'s is introduced‎. ‎In this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

one of the key problems in multi-agent coalition formation is to optimally assign and schedule resources. An improved multi-objective evolutionary Algorithm (IMOEA) is proposed to solve this problem. Compared with several well-known algorithms such as NSGA, MOEA, experimental results show the algorithm is very suitable for coalition formation problem.

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimiza...

Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective dMO algorithm. In...