This thesis deals with the analysis and application of evolutionary algorithms for optimization problems with multiple objectives. Many application problems involve (i) a system model that is not given in closed analytical form and (ii) multiple, often conflicting optimization criteria. Both traits hamper the application of classical optimization techniques, which require a certain structure of...

In this paper, a combination of simulated annealing (SA) and intelligent water drops (IWD) algorithm is used to solve the nonlinear/complex problem of simultaneous reconfiguration with optimal allocation (size and location) of wind turbine (WT) as a distributed generation (DG) and dynamic voltage restorer (DVR) as a distributed flexible AC transmission systems (DFACT) unit in a distribution sys...

This paper presents two meta-heuristic algorithms to solve an extended portfolio selection model. The extended model is based on the Markowitz's Model, aiming to minimize investment risk in a specified level of return. In order to get the Markowitz model close to the real conditions, different constraints were embedded on the model which resulted in a discrete and non-convex solution space. ...

xi Zusammenfassung xiii Statement of Contributions xv Acknowledgments xvii List of Symbols and Abbreviations xvii  Introduction  . Introductory Example . . . . . . . . . . . . . . . . . . . . . . . .  .. Multiobjective Problems . . . . . . . . . . . . . . . . . . .  .. Selecting the Best Solutions . . . . . . . . . . . . . . . . .  .. The Hypervolume Indicator . . . . . . . . . ...

This paper develops a first comparative study of multiobjective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective...

In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This i...

This article makes an in-depth analysis of different body strength calculation methods using fuzzy logic and evolutionary algorithm by taking the universal flat car as example. For this study, a more scientific reliable theoretical basis for structural design railway surveying mapping is considered. If expansion contraction jack are not synchronised while jacking vehicle with when freshly manuf...

There has not been a uniform agreement regarding what constitutes “good” performance for evolutionary algorithms in dynamic environments. A performance measurement method should, as a minimum, have an intuitive meaning and provide straightforward methods for statistical significance testing of comparative results. In this paper we attempt to resolve some issues related to EA performance measure...

Bio-inspired evolutionary algorithms are probabilistic search methods that mimic natural biological evolution. They show the behavior of the biological entities interacting locally with one another or with their environment to solve complex problems. This paper aims to analyze the most predominantly used bio-inspired optimization techniques that have been used for stock market prediction and he...

the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...