This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the no...
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This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of multiple models for optimization problems, and shows that it is possible to mix fast low-fidelity models for exploration and expensive high-fidelity models for exploitation. Finally, a new class of multi-objective optimizers mixing HGAs and Nash Game Theory is defined. These methods are tested for solving design optimization problems in aerodynamics. A parallel version of this approach running a cluster of PCs demonstrate the convergence speed up on an inverse nozzle problem and a high-lift problem for a multiple element airfoil. (C) 2002 Published by Elsevier Science B.V.
This paper addresses the challenging problem of Unit Commitment (UC), which involves the optimal scheduling of power generation units while adhering to numerous network operational constraints called security-constrai...
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This paper addresses the challenging problem of Unit Commitment (UC), which involves the optimal scheduling of power generation units while adhering to numerous network operational constraints called security-constrained UC (SCUC). SCUC problem aims to minimize costs subject to turning on economically efficient generators and turning off expensive ones. These operational constraints include load balancing, voltage level at buses, minimum up and down time requirements, spinning reserve, and ramp up and down constraints. The SCUC problem, subject to these operational constraints, is a complex mixed-integer nonlinear problem (MINLP). There has been a growing interest in using evolutionary algorithms (EAs) to tackle large-scale multi-objective MINLP problems in recent two decades. This paper introduces a novel approach to address the SCUC problem, which is further complicated by including network constraints. They are pioneering the integration of single and multi-objective EAs to solve the SCUC problem while incorporating AC network constraints through hybrid binary and real coded operators. The development of an ensemble algorithm that combines mixed real and binary coded operators, extended by a bidirectional coevolutionary algorithm to tackle multi-objective SCUC problems. The paper implements a new formulation based on three conflicting objective functions: cost of energy supplied, startup and shutdown costs of generators, energy loss, and voltage deviation to solve the SCUC problem. Implementing a new formulation also addresses the solution of single and multi-objective SCUC problems using a combination of proposed technical and economic objective functions. The proposed algorithm is rigorously tested on a 10-unit IEEE RTS system and a 6-unit IEEE 30-bus test system, both with and without security constraints, addressing week-ahead and day-ahead SCUC scenarios. Simulation results show that the proposed algorithm finds near-global optimal solutions compared to othe
In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an ev...
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In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish different taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis. (C) 2011 Elsevier B.V. All rights reserved.
Multi-criteria optimization problems are considered where the decision maker is unable to determine the exact weights of importance of the criteria but can provide some imprecise information about these weights. Two s...
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Multi-criteria optimization problems are considered where the decision maker is unable to determine the exact weights of importance of the criteria but can provide some imprecise information about these weights. Two solution concepts are studied in this framework: the optimistic min-max solution and the compromise utilitarian solution, both of which can be exactly computed for linear problems. For general problems, it is shown that these solutions can be approximated by means of a slight modification of the evolutionary algorithm NSGA-II. (C) 2012 Elsevier Ltd. All rights reserved.
The study of optimization methods for reliability–redundancy allocation problems is a constantly changing *** algorithms are continually being designed on the basis of observations of nature,wildlife,and *** this pap...
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The study of optimization methods for reliability–redundancy allocation problems is a constantly changing *** algorithms are continually being designed on the basis of observations of nature,wildlife,and *** this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation *** from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability ***,this improvement has been difficult to achieve for high-reliability *** and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.
This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach ...
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This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach employs a single reference point to express the preferences of a decision maker, and adaptively biases the search procedure toward the region of the Pareto-optimal front that best matches its expectations. Experimental results suggest that incorporating preferences within these algorithms leads to improvements in several quality criteria, and that the proposed approach is capable of yielding competitive results when compared against existing algorithms. (C) 2015 Elsevier Inc. All rights reserved.
evolutionary algorithms (EA) have been shown to be very effective in solving practical problems, yet many important theoretical issues of them are not clear. The expected first hitting time is one of the most importan...
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evolutionary algorithms (EA) have been shown to be very effective in solving practical problems, yet many important theoretical issues of them are not clear. The expected first hitting time is one of the most important theoretical issues of evolutionary algorithms, since it implies the average computational time complexity. In this paper, we establish a bridge between the expected first hitting time and another important theoretical issue, i.e., convergence rate. Through this bridge, we propose a new general approach to estimating the expected first hitting time. Using this approach, we analyze EAs with different configurations, including three mutation operators, with/without population, a recombination operator and a time variant mutation operator, on a hard problem. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms. Moreover, we give an explanation of what makes a problem hard to EAs, and based on the recognition, we prove the hardness of a general problem. (C) 2008 Published by Elsevier B.V.
This work discusses robustness assessment during multi-objective optimization with a Multi-Objective evolutionary Algorithm (MOEA) using a combination of two types of robustness measures. Expectation quantifies simult...
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This work discusses robustness assessment during multi-objective optimization with a Multi-Objective evolutionary Algorithm (MOEA) using a combination of two types of robustness measures. Expectation quantifies simultaneously fitness and robustness, while variance assesses the deviation of the original fitness in the neighborhood of the solution. Possible equations for each type are assessed via application to several benchmark problems and the selection of the most adequate is carried out. Diverse combinations of expectation and variance measures are then linked to a specific MOEA proposed by the authors, their selection being done on the basis of the results produced for various multi-objective benchmark problems. Finally, the combination preferred plus the same MOEA are used successfully to obtain the fittest and most robust Pareto optimal frontiers for a few more complex multi-criteria optimization problems.
A marketing decision support system (MDSS) is presented. It has a user-friendly and easy to learn menu driven interface. Its purpose is to assist a marketing manager in designing a line of substitute products. Optimal...
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A marketing decision support system (MDSS) is presented. It has a user-friendly and easy to learn menu driven interface. Its purpose is to assist a marketing manager in designing a line of substitute products. Optimal product line design is a very important marketing decision. The MDSS uses three different optimization criteria. It examines different scenarios using the "What if analysis". Also, it finds optimal solutions only for small sized problems using the complete enumeration method and near optimal solutions for real sized problems using evolutionary algorithms. The user is not forced to be familiar with the underlying models. (C) 2003 Elsevier B.V. All rights reserved.
Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission a...
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Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission are very problematical. So, encryption of the transmitted data is one of the most promising solutions. In our work, we focus on the security of image data, which are considered as specific data because of their big size and their information which are of two-dimensional nature and also redundant. These data characteristics make the developed algorithms in the literature unavailable in their classical forms, because of the speed and the possible risk of information loss. In this paper, we develop an original "images encryption'' algorithm based on evolutionary algorithms. The appropriateness of the proposed scheme is demonstrated by the sensitivity to images, the key and the resistibility to various advanced attacks.
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