The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However...
详细信息
The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based on computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002) [8] showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multimodal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems. (C) 2011 Elsevier B.V. All rights reserved.
This paper presents a methodology based on generic evolutionary algorithms to solve a dynamic pickup and delivery problem formulated under a hybrid predictive control approach. The solution scheme is designed to suppo...
详细信息
This paper presents a methodology based on generic evolutionary algorithms to solve a dynamic pickup and delivery problem formulated under a hybrid predictive control approach. The solution scheme is designed to support the dispatcher of a dial-a-ride service, where quick and efficient real-time solutions are needed. The scheme considers different configurations of particle swarm optimization and genetic algorithms within a proposed ad-hoc methodology to solve in real time the nonlinear mixed-integer optimization problem related with the hybrid predictive control approach. These consist of different techniques to handle the operational constraints (penalization, Baldwinian, and Lamarckian repair) and encodings (continuous and integer). For parameter tuning, a new approach based on multiobjective optimization is proposed and used to select and study some of the evolutionary algorithms. The multiobjective feature arises when deciding the parameters with the best trade-off between performance and computational effort. Simulation results are presented to compare the different schemes proposed and to advise conditions for the application of the method in real instances.
This paper deals with the design of electronically steerable linear arrays for intelligent antenna systems. The design problem is modeled as a multi-objective optimization problem with non-linear constraints. The well...
详细信息
This paper deals with the design of electronically steerable linear arrays for intelligent antenna systems. The design problem is modeled as a multi-objective optimization problem with non-linear constraints. The well-known NSGA-II and SPEA 2 algorithms are employed as the methodologies to solve the resulting optimization problem. The main goal and contribution of this paper is computation of the trade-off curves between side lobe level and main beam width for steerable linear arrays. The addressed problem considers a driving-point impedance restriction placed on each element in the array. This consideration makes the problem more restrictive and therefore more difficult to solve. Experimental results show the effectiveness of the algorithms for the design of steerable linear arrays. (c) 2006 Elsevier B. V. All rights reserved.
evolutionary algorithms have been used to solve a number of variable-length problems, many of which share a common representation. A set of design variables is repeatedly defined, giving the genome a segmented structu...
详细信息
evolutionary algorithms have been used to solve a number of variable-length problems, many of which share a common representation. A set of design variables is repeatedly defined, giving the genome a segmented structure. Each segment encodes a portion, frequently a single component, of the solution. For example, in a wind farm design problem each segment may encode the position and height of a single turbine. This is described as a metameric representation, with each segment referred to as a metavariable. The number of metavariables can vary among solutions, requiring modifications to the traditional fixed-length evolutionary operators. This paper surveys the literature that uses metameric representations with a focus on the problems being solved, the specifics of the representation, and the modifications to evolutionary operators. While there is little cross-referencing among the cited articles, it is demonstrated that there is already a strong overlap in their methodologies. By considering problems using a metameric representation as a single class, greater recognition of commonalities and differences among these works can be achieved. This could allow for the development of more efficient metameric evolutionary algorithms.
This paper shows how evolutionary algorithms can be described in a concise, yet comprehensive and accurate way. A classification scheme is introduced and presented in a tabular form called TEA (Table of evolutionary A...
详细信息
This paper shows how evolutionary algorithms can be described in a concise, yet comprehensive and accurate way. A classification scheme is introduced and presented in a tabular form called TEA (Table of evolutionary algorithms). It distinguishes between different classes of evolutionary algorithms (e.g., genetic algorithms, ant systems) by enumerating the fundamental ingredients of each of these algorithms. At the end, possible uses of the TEA are illustrated on classical evolutionary algorithms.
The widespread use and applicability of evolutionary algorithms is due in part to the ability to adapt them to a particular problem-solving context by tuning their parameters. This is one of the problems that a user f...
详细信息
The widespread use and applicability of evolutionary algorithms is due in part to the ability to adapt them to a particular problem-solving context by tuning their parameters. This is one of the problems that a user faces when applying an evolutionary Algorithm to solve a given problem. Before running the algorithm, the user typically has to specify values for a number of parameters, such as population size, selection rate, and probability operators. This paper empirically assesses the performance of an automatic parameter tuning system in order to avoid the problems of time requirements and the interaction of parameters. The system, based on Bayesian Networks and Case-Based Reasoning methodology, estimates the best parameter setting for maximizing the performance of evolutionary algorithms. The algorithms are applied to solve a basic problem in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The experimental results demonstrate the validity of the proposed system and its potential effectiveness for configuring algorithms. (C) 2014 Elsevier B.V. All rights reserved.
Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then ra...
详细信息
Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposition-based learning to generate initial population. The conducted experiments over a comprehensive set of benchmark functions demonstrate that replacing the random initialization with the opposition-based population initialization can accelerate convergence speed. (C) 2007 Elsevier Ltd. All rights reserved.
Recently, many evolutionary algorithms have been proposed. Compared to other algorithms, the core of the many-objective evolutionary algorithm using a one-by-one selection strategy is to select offspring one by one in...
详细信息
Recently, many evolutionary algorithms have been proposed. Compared to other algorithms, the core of the many-objective evolutionary algorithm using a one-by-one selection strategy is to select offspring one by one in environmental selection. However, it does not perform well in resolving large-scale many-objective optimization problems. In addition, a large amount of meaningful information in the population of the previous iteration is not retained. The information feedback model is an effective strategy to reuse the information from previous populations and integrate it into the update process of the offspring. Based on the original algorithm, this paper proposes a series of many-objective evolutionary algorithms, including six new algorithms. Experiments were carried out in three different aspects. Using the same nine benchmark problems, we compared the original algorithm with six new algorithms. algorithms with excellent performance were selected and compared with the latest studies using the information feedback model from two aspects. Then, the best one was selected for comparison with six state-of-the-art many-objective evolutionary algorithms. Additionally, non-parametric statistical tests were conducted to evaluate the different algorithms. The comparison, with up to 15 objectives and 1500 decision variables, showed that the proposed algorithm achieved the best performance, indicating its strong competitiveness.
This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous computing environments, a NP-hard problem with capital relevance in distributed computing. Th...
详细信息
This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous computing environments, a NP-hard problem with capital relevance in distributed computing. These methods have been specifically designed to provide accurate and efficient solutions by using simple operators that allow them to be later extended for solving realistic problem instances arising in distributed heterogeneous computing (HC) and grid systems. The EAs were codified over MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on well-known problem instances. The comparative study of scheduling methods shows that the parallel versions of the implemented evolutionary algorithms are able to achieve high problem solving efficacy, outperforming traditional scheduling heuristics and also improving over previous results already reported in the related literature.
This paper proposes a novel adaptive nesting evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operate...
详细信息
This paper proposes a novel adaptive nesting evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operated in time (specifically, the charging and discharging scheduling of the Energy Storage System, ESS, elements). For this purpose, a real MG scenario consisting of a wind and a photovoltaic generator, an ESS made up of one electrochemical battery, and residential and industrial loads is considered. Optimization is addressed by nesting a two-steps procedure [the first step optimizes the structure using an evolutionary Algorithm (EA), and the second step optimizes the scheduling using another EA] following different adaptive approaches that determine the number of fitness function evaluations to perform in each EA. Finally, results obtained are compared to non-nesting 2-steps algorithm evolving following a classical scheme. Results obtained show a 3.5 % improvement with respect to the baseline scenario (the non-nesting 2-steps algorithm), or a 21 % improvement when the initial solution obtained with the Baseline Charge and Discharge Procedure is used as reference.
暂无评论