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algorithm portfolio based scheme for dynamic optimization problems

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2015年第4期8卷 667-689页

作者： Fajardo Calderin, Jenny Masegosa, Antonio D. Pelta, David A. Polytech Higher Inst Jose A Echeverria Dept Artificial Intelligence & Infrastruct & Syst Havana Cuba Univ Deusto Deusto Inst Technol Bilbao Spain Basque Fdn Sci Ikerbasque Bilbao Spain Univ Granada Dept Comp Sci & AI Res Ctr ICT CITIC UGR Granada Spain

Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in Dynamic Optimization Problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic algorithm and Self Organized Random Immigrants Genetic algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.

关键词： learning algorithm portfolio algorithm selection problem combinatorial problems dynamic optimization problems

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algorithm portfolio for Parameter Tuned Evolutionary algorithms

Algorithm Portfolio for Parameter Tuned Evolutionary Algorit...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Tong, Hao Zhang, Shuyi Huang, Changwu Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728124858

Evolutionary algorithms' performance can be enhanced significantly by using suitable parameter configurations when solving optimization problems. Most existing parametertuning methods are inefficient, which tune algorithm's parameters using whole benchmark function set and only obtain one parameter configuration. Moreover, the only obtained parameter configuration is likely to fail when solving different problems. In this paper, we propose a framework that applying portfolio for parameter-tuned algorithm (PPTA) to address these challenges. PPTA uses the parameter-tuned algorithm to tune algorithm's parameters on one instance of each problem category, but not to all functions in the benchmark. As a result, it can obtain one parameter configuration for each problem category. Then, PPTA combines several instantiations of the same algorithms with different tuned parameters by portfolio method to decrease the risk of solving unknown problems. In order to analyse the performance of PPTA framework, we embed several test algorithms (i.e. GA, DE and PSO) into PPTA framework constructing algorithm instances. And the PPTA instances are compared with default test algorithms on BBOB2009 and CEC2005 benchmark functions. The experimental results has shown PPTA framework can significantly enhance the basic algorithm's performance and reduce its optimization risk as well as the algorithm's parametertuning time.

关键词： Knowledge transfer Auto parameter tuning algorithm portfolio Evolutionary algorithm

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algorithm portfolio for Individual-based Surrogate-Assisted Evolutionary algorithms

Algorithm Portfolio for Individual-based Surrogate-Assisted ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Tong, Hao Liu, Jialin Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Key Lab Computat Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781450361118

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.

关键词： algorithm portfolio surrogate model evolutionary algorithm computationally expensive problem

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Which algorithm should I choose: An evolutionary algorithm portfolio approach

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APPLIED SOFT COMPUTING 2016年 40卷 654-673页

作者： Yuen, Shiu Yin Chow, Chi Kin Zhang, Xin Lou, Yang City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem for single objective optimization. A portfolio of evolutionary algorithms is first formed. Covariance Matrix Adaptation Evolution Strategy (CMA-ES), History driven Evolutionary algorithm (HdEA), Particle Swarm Optimization (PS02011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary algorithm (MultiEA). The predictor we introduced has the nice property of being parameter-less, and algorithms switch automatically as a function of budget. The following contributions are made: (1) experimental results on 24 benchmark functions show that MultiEA outperforms (i) Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO);(ii) Population-based algorithm portfolio (PAP);(iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA);and (iv) a multiple algorithm approach which divides the computational budget evenly and execute all algorithms in parallel (ExhEA). This shows that it outperforms existing portfolio approaches and the predictor is functioning well. (2) Moreover, a neck to neck comparison of MultiEA with CMA-ES, HdEA, PS02011, and SaDE is also made. Experimental results show that the performance of MultiEA is very competitive. In particular, MultiEA, being a portfolio algorithm, is sometimes even better

关键词： Multi-method search algorithm portfolio Performance prediction Synergy Scalability Real world application

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An algorithm portfolio for the dynamic maximal covering location problem

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MEMETIC COMPUTING 2017年第2期9卷 141-151页

作者： Calderin, Jenny Fajardo Masegosa, Antonio D. Pelta, David A. Polytech Higher Inst Jose A Echeverria Dept Articial Intelligence & Infrastruct & Syst Havana Cuba Univ Deusto Fac Engn Bilbao 48007 Spain Deusto Fdn DeustoTech Fdn Deusto Bilbao 48007 Spain Ikerbasque Basque Fdn Sci E-48011 Bilbao Spain Univ Granada Dept Comp Sci & AI Granada Spain

The location of facilities (antennas, ambulances, police patrols, etc) has been widely studied in the literature. The maximal covering location problem aims at locating the facilities in such positions that maximizes certain notion of coverage. In the dynamic or multi-period version of the problem, it is assumed that the nodes' demand changes with the time and as a consequence, facilities can be opened or closed among the periods. In this contribution we propose to solve dynamic maximal covering location problem using an algorithm portfolio that includes adaptation, cooperation and learning. The portfolio is composed of an evolutionary strategy and three different simulated annealing methods (that were recently used to solve the problem). Experiments were conducted on 45 test instances (considering up to 2500 nodes and 200 potential facility locations). The results clearly show that the performance of the portfolio is significantly better than its constituent algorithms.

关键词： Coverage Location Metaheuristics algorithm portfolio

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A sequential algorithm portfolio approach for black box optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 559-570页

作者： He, Yaodong Yuen, Shiu Yin Lou, Yang Zhang, Xin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

关键词： algorithm portfolio algorithm selection Heuristic algorithms Optimization problems Performance prediction

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An algorithm portfolio based solution methodology to solve a supply chain optimization problem

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 8407-8420页

作者： Yadav, Salik R. Muddada, Raja Ram M. R. Tiwari, M. K. Shankar, Ravi Indian Inst Technol Dept Ind Engn & Management Kharagpur 721302 W Bengal India Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA Indian Inst Technol Dept Management Studies New Delhi 110016 India

This paper introduces the algorithm portfolio concept to solve a combinatorial optimization problem pertaining to a supply chain. The Supply chain problem is modeled with capacity constraints and demand variations over different time periods to minimize the total supply chain configuration cost. The algorithm portfolio is implemented over Various problem instances to inspect and alleviate the computational expensiveness of a solution strategy. A bunch of five algorithms are utilized hereby viz. AIS, GA, Endosymbiotic Optimization, PSO and Psychoclonal algorithm. The observations reflect the appropriateness and effect of algorithm portfolios over the adopted supply chain, and viability over other optimization problems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Supply chain optimization algorithm portfolio Platform and independent supply chain

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An algorithm portfolio approach to reconfigurable set-up planning

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2011年第8期24卷 756-768页

作者： Prasoon, Ruchir Das, Devashish Tiwari, Manoj Kumar Wang, Lihui Indian Inst Technol Dept Ind Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India Univ Skovde Virtual Syst Res Ctr Skovde Sweden Univ Skovde Ctr Intelligent Automat Skovde Sweden

This article discusses an algorithm portfolio approach to find optimal set-up plans in a dynamic shop floor environment where flexibility and promptness of the decision process is critical along with best possible utilisation of the available resources. An evolutionary algorithm based reconfigurable set-up planning approach is presented where the final set-up plan is determined in two steps: primitive set-up planning through feature grouping and reconfigurable set-up merging based on real time information from the scheduling system. The tendency of single algorithm approach to converge to sub-optimal solutions was countered by using portfolios of genetic algorithm and its three variants: Genetic algorithm with Chromosome Differentiation, Sexual Genetic algorithm and a modified version of Age Genetic algorithm. Best performing portfolios selected after exhaustive experimentation showed dramatic computational improvements in achieving the optimal solution validating the appropriateness and effectiveness of algorithm portfolio approach.

关键词： reconfigurable set-up planning algorithm portfolio CNC machining

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algorithm portfolios for logistics optimization considering stochastic demands and mobility allowance

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 146-166页

作者： Shukla, Nagesh Choudhary, A. K. Prakash, P. K. S. Fernandes, K. J. Tiwari, M. K. Univ Sheffield Logist & Supply Chain Res Ctr Sch Management Sheffield S10 2TN S Yorkshire England Univ Wollongong SMART Infrastruct Facil Wollongong NSW 2522 Australia Univ Warwick Digital Lab WMG Coventry CV4 7AL W Midlands England NUI Irish Ctr Mfg Res Maynooth Kildare Ireland Univ York York Management Sch York YO10 5DD N Yorkshire England Indian Inst Technol Dept Ind Engn & Management Kharagpur 721302 W Bengal India

The vehicle routing problem with stochastic demand (VRPSD) is a well known NP-hard problem. The uncharacteristic behaviour associated with the problem enhances the computational efforts required to obtain a feasible and near-optimal solution. This paper proposes an algorithm portfolio methodology based on evolutionary algorithms, which takes into account the stochastic nature of customer demand to solve this computationally complex problem. These problems are well known to have computationally complex objective functions, which make their solutions hard to find, particularly when problem instances of large dimensions are considered. Of particular importance in such situations is the timeliness of the solution. For example, Apple was forced to delay their shipments of iPads internationally due to unprecedented demand and issues with their delivery systems in Samsung Electronics and Seiko Epson. Such examples illustrate the importance of stochastic customer demands and the timing of delivery. Moreover, most of the evolutionary algorithms, known for providing computationally efficient solutions, are unable to always provide optimal or near optimal solutions to all the VRPSD instances within allocated time interval. This is due to the characteristic variations in the computational time taken by evolutionary algorithms for same or varying size of the VRPSD instances. Therefore, this paper presents portfolios of different evolutionary algorithms to reduce the computational time taken to resolve the VRPSD. Moreover, an innovative concept of the mobility allowance (MA) in landmoves based on the levy's distribution function has been introduced to cope with real situations existing in vehicle routing problems. The proposed portfolio approach has been evaluated for the varying instances of the VRPSD. Four of the existing metaheuristics including Genetic algorithm (GA), Simulated Annealing (SA), Artificial Immune System (AIS), TABU Search (TS) along with new neighbourhood searc

关键词： algorithm portfolio metaheuristic search Vehicle routing problem Stochastic demand Genetic algorithm Simulated annealing TABU search

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On algorithm portfolios and restart strategies

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OPERATIONS RESEARCH LETTERS 2011年第1期39卷 49-52页

作者： Shylo, Oleg V. Prokopyev, Oleg A. Rajgopal, Jayant Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

We consider two parallel strategies for randomized restart algorithms. Given a set of available algorithms, one can either choose the best performing algorithm and run multiple copies of it in parallel (single algorithm portfolio), or choose some subset of algorithms to run in parallel (mixed algorithm portfolio). It has been previously shown in the literature that the latter approach may provide better results. In this paper we investigate the extent of such improvement. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Parallel optimization Restart algorithm portfolio Las Vegas algorithm

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