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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Cybula, Piotr Jaszkiewicz, Andrzej Pelka, Przemyslaw Rogalski, Marek Sielski, Piotr Univ Lodz Fac Math & Comp Sci Banacha 22 PL-90238 Lodz Poland Poznan Univ Tech Fac Comp Piotrowo 3 PL-60965 Poznan Poland Lodz Univ Technol Fac Elect Elect Comp & Control Engn Stefanowskiego 18-22 PL-90924 Lodz Poland

ISBN: (纸本)9783031147142;9783031147135

We propose an evolutionary algorithm with a novel diversity oscillation mechanism for the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). evolutionary algorithms are among state-of-the-art methods for vehicle routing problems and the diversity management is the key component of many of these algorithms. In our algorithm the diversity level slowly oscillates between its minimum and maximum value, however, whenever a new best solution is found the algorithm switches to decreasing the diversity level in order to intensify the search in the vicinity of the new best solution. We use also an additional population of high quality diverse solutions, which may be used to re-fill the main population when the diversification level is increased. The results of the computational experiment indicate that the proposed mechanism significantly improves the performance of our hybrid evolutionary algorithm on typical CVRPTW benchmarks and that the proposed algorithm is competitive to the state-of-the-art results presented in the literature.

关键词： Diversity management Vehicle routing hybrid evolutionary algorithms Multi-population algorithms

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A hybrid evolutionary Multiobjective Approach for the Component Selection Problem

A Hybrid Evolutionary Multiobjective Approach for the Compon...

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3rd International Workshop on hybrid Artificial Intelligence Systems

作者： Vescan, Andreea Grosan, Crina Univ Babes Bolyai Dept Comp Sci Cluj Napoca 400084 Romania

ISBN: (纸本)9783540876557

Component selection is a crucial problem in Component Based Software Engineering (CBSE). CBSE is concerned with the assembly of preexisting software components that leads to a software system that responds to client-specific requirements. We formulate the problem as multiobjective, involving 3 objectives. The approach used in this paper is an evolutionary computation technique combined with a greedy algorithm which is used to fine tune the Solutions after the application of the genetic operators. The experiments and comparisons with Greedy technique show the effectiveness of the proposed approach.

关键词： Component Selection hybrid evolutionary algorithms Greedy

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Scheduling Projects by a hybrid evolutionary Algorithm with Self-Adaptive Processes 14th

Scheduling Projects by a Hybrid Evolutionary Algorithm with ...

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14th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Univ ISISTAN Res Inst RA-7000 Tandil Argentina Natl Council Sci & Technol Res CONICET Buenos Aires DF Argentina

ISBN: (纸本)9783319270609;9783319270593

In this paper, we present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.

关键词： Project scheduling Human resource assignment Multi-skilled resources hybrid evolutionary algorithms evolutionary algorithms Simulated annealing algorithms

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Facilitating the hybridization of Parallel evolutionary algorithms in Cluster Computing Environments 22

Facilitating the Hybridization of Parallel Evolutionary Algo...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Khalloof, Hatem Ciftci, Sergen Shahoud, Shadi Duepmeier, Clemens Foerderer, Kevin Hagenmeyer, Veit Karlsruhe Inst Technol IAI Karlsruhe Germany

ISBN: (纸本)9781450392686

evolutionary algorithms (EAs) need domain-specific adaptations for achieving better results, tend to converge to suboptimal solutions and are computationally expensive when they are applied to complex and large-scale optimization problems. For addressing these challenges, hybridizing EAs with other algorithms and methods and parallelizing them in cluster computing environments represent essential solutions. In the present paper, a new software solution for supporting the hybridizations of parallel EAs is proposed. Unlike other software solutions for hybridizing EAs, the proposed software solution provides a flexible, generic and scalable mechanism for integrating any algorithmic approach like a Machine Learning (ML) algorithm to seed the initial population of parallel EAs. It is designed based on three modern software technologies, namely microservices, container virtualization and the publish/subscribe messaging paradigm. The applicability and generality of the presented software solution is tested by hybridizing the General Learning evolutionary Algorithm and Method (GLEAM) with ML techniques for solving the problem of scheduling Distributed Energy Resources (DERs). The benchmarking tests are performed in a cluster computing environment. The obtained results show that the new software solution represents a successful approach to facilitate the hybridization of parallel EAs paving the road for future applications of EAs in several domains.

关键词： hybrid evolutionary algorithms Parallel evolutionary algorithms Global Model Microservice Virtualization Container Scheduling Distributed Energy Resources Parallel Computing Scalability

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A Memetic evolutionary Algorithm for Real-Time Articulated Kinematic Motion

A Memetic Evolutionary Algorithm for Real-Time Articulated K...

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IEEE Congress on evolutionary Computation (CEC)

作者： Starke, Sebastian Hendrich, Norman Zhang, Jianwei Univ Hamburg Dept Informat Hamburg Germany

ISBN: (纸本)9781509046010

Solving kinematic motion is a challenging field of research which is relevant for various applications in character animation and robotics. This paper presents a novel and fast hybrid evolutionary algorithm for inverse kinematics which can handle fully constrained and highly articulated geometries with multiple end effectors and individual objectives. Several experiments on the 42 DoF human body mannequin and other kinematic models demonstrate a robust multimodal and multiobjective optimisation, and the ability to evolve accurate solutions in real- time while offering maximum flexibility for the design of custom cost functions.

关键词： hybrid evolutionary algorithms Swarm Intelligence Multi-Objective Optimisation Inverse Kinematics Robotics Character Animation Games

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Generic limnological models on the touchstone:: Testing the lake simulation library SALMO-OO and the rule-based Microcystis agent for warm-monomictic hypertrophic lakes in South Africa

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ECOLOGICAL MODELLING 2008年第1-3期215卷 144-158页

作者： Recknagel, Friedrich van Ginkel, Carin Cao, Hongqing Cetin, Lydia Zhang, Byron Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia Dept Water Affairs & Forestry ZA-0001 Pretoria South Africa

Generic models are required to share models for the same category of ecosystems in order to facilitate comparative scenario analyses and forecasting, and classify ecosystem health. However, generality is the most often neglected one of the three criteria for model validation: reality;precision and generality. The paper suggests to validate models for lake ecosystem categories defined by (a) circulation types of lakes that to some degree reflect climate and morphometry and (b) trophic states of lakes that to some degree reflect community structures and habitat properties. Two models are tested for South African lakes belonging to the category of warm-monomictic and hypertrophic lakes: the deterministic simulation library SALMO-OO and a rule-based agent for Microcystis discovered by evolutionary computation. SALMO-OO is an object-oriented implementation of mass balance equations for a typical pelagic food web (diatoms, green algae, blue-green algae, and cladocerans) and nutrient cycles (PO4-P, NO3-N, detritus) as well as alternative process models for algal growth and grazing, zooplankton growth and mortality. During validation the best performing model structure is determined for a number of lakes belonging to the same category. SALMO-OO is implemented in JAVA providing an interactive user-interface. The rule-based Microcystis agent has been discovered by the hybrid evolutionary algorithm HEA after training by merged time series data of three lakes belonging to the same category and 14-fold cross-validation for data of all 14 years of each lake. The agent allows 7-days-ahead forecasting of Microcystis abundance for any of the three lakes. It can be concluded that the design and validation of models for ecosystem categories improves the generality of models, and promotes both model as well as data sharing. Future work will focus on distinctive ecological features of ecosystem categories to be considered in informed structure design and validation of category models. C

关键词： Microcystis hypertrophic warm-monomictic lakes lake simulation library SALMO-OO scenario analysis rule-based agents hybrid evolutionary algorithms forecasting

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A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects

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ENERGY CONVERSION AND MANAGEMENT 2011年第4期52卷 1800-1809页

作者： Niknam, Taher Mojarrad, Hasan Doagou Meymand, Hamed Zeinoddini Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Economic dispatch (ED) is one of the important problems in the operation and management of the electric power systems which is formulated as an optimization problem. Modern heuristics stochastic optimization techniques appear to be efficient in solving ED problem without any restriction because of their ability to seek the global optimal solution. One of modern heuristic algorithms is particle swarm optimization (PSO). In PSO algorithm, particles change place to get close to the best position and find the global minimum point. Also, differential evolution (DE) is a robust statistical method for solving non-linear and non-convex optimization problem. The fast convergence of DE degrades its performance and reduces its search capability that leads to a higher probability towards obtaining a local optimum. In order to overcome this drawback a hybrid method is presented to solve the ED problem with valve-point loading effect by integrating the variable DE with the fuzzy adaptive PSO called FAPSO-VDE. DE is the main optimizer and the PSO is used to maintain the population diversity and prevent leading to misleading local optima for every improvement in the solution of the DE run. The parameters of proposed hybrid algorithm such as inertia weight, mutation and crossover factors are adaptively adjusted. The feasibility and effectiveness of the proposed hybrid algorithm is demonstrated for two case studies and results are compared with those of other methods. It is shown that FAPSO-VDE has high quality solution, superior convergence characteristics and shorter computation time. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Variable DE Fuzzy adaptive particle swarm optimization hybrid evolutionary algorithms

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Three-dimensional protein structure prediction based on memetic algorithms

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COMPUTERS & OPERATIONS RESEARCH 2018年 91卷 160-177页

作者： Correa, Leonardo de Lima Borguesan, Bruno Krause, Mathias J. Dorn, Marcio Fed Univ Rio Grande Sul UFRGS Inst Informat INF Av Bento Gongalves 9500 Porto Alegre RS Brazil Karlsruhe Inst Technol Inst Appl & Numer Math Inst Mech Proc Engn & Mech MVM D-76131 Karlsruhe Germany

Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as a NP-hard problem. In this paper, we proposed a first-principle method that makes use of a priori information about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multi modal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in the Protein Data Bank to guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Metaheuristics hybrid evolutionary algorithms Multimodal optimization Structural Bioinformatics PSP problem

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An improved GA and a novel PSO-GA-based hybrid algorithm

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INFORMATION PROCESSING LETTERS 2005年第5期93卷 255-261页

作者： Shi, XH Liang, YC Lee, HP Lu, C Wang, LM Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Inst High Performance Comp Singapore 117528 Singapore Changchun Taxat Coll Dept Comp Sci & Technol Changchun 130021 Peoples R China

Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the "dying probability" for the individuals and the "war/disease process" for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results show that both VPGA and PGHA are effective for the optimization problems. (C) 2004 Elsevier B.V. All rights reserved.

关键词： algorithms genetic algorithms particle swarm optimization hybrid evolutionary algorithms optimization

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Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution

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ENGINEERING OPTIMIZATION 2018年第10期50卷 1645-1661页

作者： Panagant, Natee Bureerat, Sujin Khon Kaen Univ Fac Engn Sustainable & Infrastruct Res & Dev Ctr Dept Mech Engn Khon Kaen Thailand

A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article. hybrid reproduction and control parameter adaptation strategies are employed to increase the performance of the algorithm. The proposed algorithm, called fully stressed design-grey wolf-adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems. The problems have mixed continuous/discrete design variables that are assigned as simultaneous topology, shape and sizing design variables. FSD-GWADE provides competitive results and gives superior results at a higher success rate than the previous FSD-based algorithm.

关键词： Truss optimization differential evolution grey wolf optimization hybrid evolutionary algorithms adaptive evolutionary algorithms fully stressed design

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