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3rd UKSim European Symposium on Computer Modeling and Simulation

作者： El Hamdi, R. Njah, M. Chtourou, M. Univ Sfax Sfax Engn Sch Res Unit Intelligent Control Design & Optimizat C Sfax 3038 Tunisia

ISBN: (纸本)9781424453450

This work discusses how Radial Basis Function (RBF) neural networks can have their free parameters defined by evolutionary algorithms (EAs). For such, it firstly presents an overall view of the problems involved and the different evolutionary approaches used to optimize RBF networks. It also proposes a Memetic (ie. evolutionary algorithms (EAs) augmented with local search) RBF networks (MRBF) that adopts the most sequential training algorithm, where weights are updated after each training pattern is presented to the network, to elite individuals (having best fitness) and the so-called batch training mode to the remaining individuals of the population. Experiments using a benchmark problem are performed and the results achieved, using the proposed EA, are compared to those achieved by other approaches. The proposed techniques are quite general and may also be applied to a large range of learning algorithms.

关键词： radial basis function learning hybrid evolutionary algorithm

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A hybrid evolutionary Approach with Search Strategy Adaptation for Mutiobjective Optimization 13

A Hybrid Evolutionary Approach with Search Strategy Adaptati...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Kafafy, Ahmed Bonnevay, Stephane Bounekkar, Ahmed Univ Lyon Lab ERIC F-69676 Bron France

ISBN: (纸本)9781450319638

hybrid evolutionary algorithms have been successfully applied to solve numerous multiobjective optimization problems (MOP). In this paper, a new hybrid evolutionary approach based on search strategy adaptation (HESSA) is presented. In HESSA, the search process is carried out through adopting a pool of different search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected strategy. Then, according to the success of the generated offspring to update the population or the archive, the success ratio of the selected strategy is adapted. This provides the ability for HESSA to adopt the appropriate search strategy according to the problem on hand. Furthermore, the cooperation among different strategies leads to improve the exploration and the exploitation of the search space. The proposed pool is combined to a suitable evolutionary framework for supporting the integration and cooperation. Moreover, the efficient solutions explored over the search are collected in an external repository to be used as global guides. The proposed HESSA is verified against some of the state of the art MOEAs using a set of test problems commonly used in the literature. The experimental results indicate that HESSA is highly competitive and can be considered as a viable alternative.

关键词： Multiobjective Optimization hybrid evolutionary algorithm Search Strategy adaptation

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Combining wavelet transform and the evolutionary neural network for radar target recognition

Combining wavelet transform and the evolutionary neural netw...

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Conference on Wavelet Applications VII

作者： Li, Y Jiao, LC Bai, BD Xidian Univ Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)0819436828

This paper introduces an approach for radar target recognition by the range profiles based on the combination of wavelet transform and the evolutionary neural network. The whole recognition process consists of two main stages. The first stage is concerned with feature extraction where the goal is to find the small number of features from high feature space that retains all information needed for an accurate recognition. The second stage is concerned with classification of the patterns based on their reduced features after feature extraction. As the radar echo, i.e., the range profile is a nonstationary signal, Mallat algorithm is used to select and compress the features of the range profiles in the first stage. In the second stage, We use an evolutionary neural network as classifier, that is, construct a feed-forward neural network as classifier by using a hybrid evolutionary algorithm based on evolutionary programming. The experimental results show the whole target recognition system has simple structure and good generalization ability.

关键词： wavelet transform evolutionary neural network hybrid evolutionary algorithm radar target recognition

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evolutionary Non-linear Great Deluge for University Course Timetabling

Evolutionary Non-linear Great Deluge for University Course T...

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4th International Workshop on hybrid Artificial Intelligence Systems

作者： Landa-Silva, Dario Obit, Joe Henry Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning Res Grp Nottingham NG7 2RD England

ISBN: (纸本)9783642023187

This paper presents a hybrid evolutionary algorithm to tackle university course timetabling problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment;problems. That initialisation process is capable of producing feasible solutions even for the large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conduct experiments to evaluate the performance of the proposed hybrid algorithm and in particular, the contribution of the evolutionary operators. Our results show that the hybrid between non-linear great deluge and evolutionary operators produces very good results oil the instances of the university course timetabling problem tackled here.

关键词： hybrid evolutionary algorithm non-linear great deluge course timetabling

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An evolutionary Optimized Device for Energy Harvesting from Traffic

An Evolutionary Optimized Device for Energy Harvesting from ...

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IEEE Congress on evolutionary Computation (CEC)

作者： Pirisi, Andrea Grimaccia, F. Mussetta, M. Zich, R. E. UP Underground Power Via Garibaldi 144 Milan Italy Politecn Milan Dipartimento Engn I-20156 Milan Italy

ISBN: (纸本)9781467315098

In recent years the increase of the computational capability and the development of innovative multi-physics techniques has determined a growing interest towards modeling and optimization in engineering system design and green energy applications. In this context, advanced soft computing techniques can be applied by engineers to several problems and used within optimization process, in order to find out the best design and to improve the system performance. These techniques promise also to give new impulse to research on renewable systems and, especially in the last five year, on the so called Energy Harvesting Devices (EHDs). In this paper the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from traffic applications is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment. Finally, an experimental validation of the designed EHD prototype is presented.

关键词： hybrid evolutionary algorithm tubular linear generator optimization energy harvesting

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Neighborhood Combination Search for Single-Machine Scheduling with Sequence-Dependent Setup Time

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Journal of Computer Science & Technology 2024年第3期39卷 737-752页

作者：刘晓路徐宏云陈嘉铭苏宙行吕志鹏丁俊文 College of System Engineering National University of Defense TechnologyChangsha 410015China School of Artificial Intelligence Jianghan UniversityWuhan 430056China Xi’an Satellite Control Center Xi’an 710043China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China CCF

In a local search algorithm,one of its most important features is the definition of its neighborhood which is crucial to the algorithm's *** this paper,we present an analysis of neighborhood combination search for solv-ing the single-machine scheduling problem with sequence-dependent setup time with the objective of minimizing total weighted tardiness(SMSWT).First,We propose a new neighborhood structure named Block Swap(B1)which can be con-sidered as an extension of the previously widely used Block Move(B2)neighborhood,and a fast incremental evaluation technique to enhance its evaluation ***,based on the Block Swap and Block Move neighborhoods,we present two kinds of neighborhood structures:neighborhood union(denoted by B1UB2)and token-ring search(denoted by B1→B2),both of which are combinations of B1 and ***,we incorporate the neighborhood union and token-ring search into two representative metaheuristic algorithms:the Iterated Local Search algorithm(ILSnew)and the hybrid evolutionary algorithm(HEA_(new))to investigate the performance of the neighborhood union and token-ring ***-sive experiments show the competitiveness of the token-ring search combination mechanism of the two *** on the 120 public benchmark instances,our HEA_(new)has a highly competitive performance in solution quality and computational time compared with both the exact algorithms and recent *** have also tested the HEA,new algorithm with the selected neighborhood combination search to deal with the 64 public benchmark instances of the single-machine scheduling problem with sequence-dependent setup *** is able to match the optimal or the best known results for all the 64 *** particular,the computational time for reaching the best well-known results for five chal-lenging instances is reduced by at least 61.25%.

关键词： single-machine scheduling sequence-dependent setup time neighborhood combination search token-ring search hybrid evolutionary algorithm

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Two-Level Production Plan Decomposition Based on a hybrid MOEA for Mineral Processing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2013年第4期10卷 1050-1071页

作者： Yu, Gang Chai, Tianyou Luo, Xiaochuan Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Ctr Automat Res Shenyang 110004 Peoples R China

This paper addresses the integrated multiobjective production planning and decomposing (P&D) problem for mineral processing. A novel multiobjective 0-1 mixed integer nonlinear programming model is presented for the simultaneous P&D problem (O-model). In order to reduce the computational cost for solving O-model, a rolling horizon-based two-level decomposition approach is proposed to separate O-model into an upper level model (H-model) and a lower level model (L-model). An interactive partition (IP) and multiobjective gradient (MO-G)-based hybrid evolutionary multiobjective (EMO) algorithm named as IG-NSGA-II/IG-SPEA2, which takes the popular NSGA-II/SPEA2 as the basic EA, is proposed to solve both H-model and L-model, where an IP technique is designed to generate the efficient feasible combinational nodes, an ideal solution technique is provided for fathoming the infeasible nodes, an improved multiobjective gradient-based operator is developed to accelerate the evolution process in each selected node, and a cut with all continuous variables is constructed to exclude the previous feasible combination if it is not desired for the decision makers (DMs). The experimental results demonstrate that the presented two-level decomposition strategy can effectively integrate both levels. Moreover, the proposed hybrid method can effectively reduce the combinatorial space so as to concentrate the computing resource on the subspace of most interest, and can generate better feasible solutions than the pure EA in the full-space under computation time limits.

关键词： hybrid evolutionary algorithm integrated planning and decomposing interactive partition multiobjective gradient-based operator production indicators optimization rolling horizon-based two-level decomposing strategy

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A model induced max-min ant colony optimization for asymmetric traveling salesman problem

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APPLIED SOFT COMPUTING 2013年第3期13卷 1365-1375页

作者： Bai, Jie Yang, Gen-Ke Chen, Yu-Wang Hu, Li-Sheng Pan, Chang-Chun Shanghai Jiao Tong Univ Minist Educ China Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Manchester MBS Decis & Cognit Sci Res Ctr Manchester M15 6PB Lancs England

A large number of hybrid metaheuristics for asymmetric traveling salesman problem (ATSP) have been proposed in the past decades which produced better solutions by exploiting the complementary characteristics of different optimization strategies. However, most of the hybridizations are criticized due to lacking of sufficient analytical basis. In this paper, a model induced max-min ant colony optimization (MIMM-ACO) is proposed to bridge the gap between hybridizations and theoretical analysis. The proposed method exploits analytical knowledge from both the ATSP model and the dynamics of ACO guiding the behavior of ants which forms the theoretical basis for the hybridization. The contribution of this paper mainly includes three supporting propositions that lead to two improvements in comparison with classical max-min ACO optimization (MM-ACO): (1) Adjusted transition probabilities are developed by replacing the static biased weighting factors with the dynamic ones which are determined by the partial solution that ant has constructed. As a byproduct, nonoptimal arcs will be indentified and excluded from further consideration based on the dual information derived from solving the associated assignment problem (AP). (2) A terminal condition is determined analytically based on the state of pheromone matrix structure rather than intuitively as in most traditional hybrid metaheuristics. Apart from the theoretical analysis, we experimentally show that the proposed algorithm exhibits more powerful searching ability than classical MM-ACO and outperforms state of art hybrid metaheuristics. (C) 2012 Elsevier B. V. All rights reserved.

关键词： hybrid evolutionary algorithm Ant colony optimization ATSP Model induced

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A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential

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APPLIED SOFT COMPUTING 2009年第2期9卷 738-745页

作者： Kaempf, Jerome Henri Robinson, Darren Ecole Polytech Fed Lausanne Solar Energy & Bldg Phys Lab CH-1015 Lausanne Switzerland

This paper describes the results of initial experiments to apply computational algorithms to explore a large parameter space containing many variables in the search for an optimal solution for the sustainable design of an urban development using a potentially complicated fitness function. This initial work concentrates on varying the placement of buildings to optimise solar irradiation availability. For this we propose a hybrid of the covariance matrix adaptation evolution strategy (CMA-ES) and hybrid differential evolution (HDE) algorithms coupled with an efficient backwards ray tracing technique. In this paper we concentrate on the formulation of the new hybrid algorithm and its testing using standard benchmarks as well as a solar optimisation problem. The new algorithm outperforms both the standalone CMA-ES and HDE algorithms in benchmark tests and an alternative multi-objective optimisation tool in the case of the solar optimisation problem. (C) 2008 Elsevier B. V. All rights reserved.

关键词： Optimisation Covariance matrix adaptation evolution strategy (CMA-ES) Differential evolution (DE) hybrid differential evolution (HDE) hybrid evolutionary algorithm hybrid CMA-ES/HDE Urban planning Solar energy potential

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An Adaptive Surrogate Assisted CE-QUAL-W2 Model Embedded in hybrid NSGA-II_ AMOSA algorithm for Reservoir Water Quality and Quantity Management

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WATER RESOURCES MANAGEMENT 2020年第4期34卷 1437-1451页

作者： Saadatpour, Motahareh Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

The Meimeh dam construction is a project, planned to provide sustainable livelihoods, social, and economic developments in the Meimeh River Basin, Ilam, Iran. However, due to the high concentration of TDS in the Meimeh River and its tributaries, river impoundment and water storage can be harmful to the Meimeh Reservoir. The upstream inflow control and the reservoir operation management in a selective withdrawal scheme (SWS) were used to mitigate the potential environmental degradation of Meimeh Reservoir's low water quality. CE-QUAL-W2 and WEAP (Water Evaluation and Assessment Programming) models were employed to evaluate the effects of various upstream saline inflow control scenarios. The analysis indicated that the diversion of the Siyoul tributary flow rate in the Meimeh River could result in lower violations of Total Dissolved Solids (TDS) concentrations and better water supply satisfaction. Then, the optimal reservoir operation management strategies in a SWS were derived in the best upstream inflow control scenario. The adaptive surrogate-assisted WQSM (water quality simulation model), coupled to hybrid NSGA-II_AMOSA (Non-dominated Sorting Genetic algorithm-II_Archived Multi-Objective Simulated Annealing) algorithm, has been applied to derive the suitable reservoir operation strategies in SWS, improve the water supply satisfaction and alleviate the adverse effects of reservoir outflow TDS violations. The performances of the best water quality and supply scenarios have been compared with the scenario based on the standard operation policy (SOP) in the Meimeh Reservoir. The results show that most violations of TDS criteria occur during the peak agricultural seasons and significant water deficit in some dry years happens in the SOP.

关键词： AMOSA CE-QUAL-W2 hybrid evolutionary algorithm Meimeh river-basin NSGA-II WEAP

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