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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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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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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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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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Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2006年第11期44卷 2245-2263页

作者： Kumar, V. Prakash Tiwari, M. K. Chan, F. T. S. Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Natl Inst Foundry & Forge Technol Dept Forge Technol Ranchi 834003 Bihar India Natl Inst Foundry & Forge Technol Dept Met & Mat Engn Ranchi 834003 Bihar India

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing ( SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution.

关键词： make-to-stock inventory hybrid evolutionary algorithm endosymbiotic-psychoclonal integrated steel manufacturer

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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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Stochastic programming model and algorithm for flight schedule optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期41卷 5087-5098页

作者： Zhang, Ying Yinge, A. Wang, Bing Tian, Wen Wen, Tao Nanjing Univ Aeronaut & Astronaut Natl Key Lab Air Traff Flow Management Mailbox 1007Jiangning Campus Nanjing Peoples R China

To solve the strategic flight schedule optimization problem for multiple airport and multiple operation days, a two-stage stochastic programming model is established. Flight schedule optimization is made in the first stage of the model, and the tactical flight delay decision is made in the second stage with consideration of the impact of the uncertain operational airport capacity at the tactical stage. The arrival and departure service rates are also optimized as decision variables in the second stage so as to simulate the actual operation characteristics of the airports and more accurately estimate the operation delay at the tactical stage. The model's objective function considers the minimum deviation of flight scheduled time in the strategic stage and the expected delay in the tactical stage. A hybrid evolutionary algorithm is designed to solve the model by two-stage decomposition. The model is simulated and verified using the flight plan data of Beijing Capital Airport and Guangzhou Baiyun Airport. The effect of the model in optimizing flight scheduled time and reducing the mean value of operational delay in the tactical phase under different capacity scenarios is analyzed, and the effectiveness of the model and the hybrid evolutionary algorithm is verified.

关键词： Flight schedule optimization uncertainty airport capacity hybrid evolutionary algorithm sample average approximation

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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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