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Two-stage based ensemble optimization framework for large-scale global optimization

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第2期228卷 308-320页

作者： Wang, Yu Huang, Jin Dong, Wei Shan Yan, Jun Chi Tian, Chun Hua Li, Min Mo, Wen Ting IBM Res China Beijing Peoples R China

Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. In order to strengthen both effectiveness and efficiency of LSGO algorithm, this paper designs a two-stage based ensemble optimization evolutionary algorithm (EOEA) framework, which serially implements two sub-optimizers. These two sub-optimizers mainly focus on exploration and exploitation separately. The EOEA framework can be easily generated, flexibly altered and modified, according to different implementation conditions. In order to analyze the effects of EOEA's components, we compare its performance on diverse kinds of problems with its two sub-optimizers and three variants. To show its superiorities over the previous LSGO algorithms, we compare its performance with six classical LSGO algorithms on the LSGO test functions of IEEE Congress of evolutionary Computation (CEC 2008). The performance of EOEA is further evaluated by experimental comparison with four state-of-the-art LSGO algorithms on the test functions of CEC 2010 LSGO competition. To benchmark the practical applicability of EOEA, we adopt EOEA to the parameter calibration problem of water pipeline system. Based on the experimental results on diverse scales of systems, EOEA performs steadily and robustly. (C) 2012 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Continuous optimization Large scale global optimization Cooperative co-evolution Gradual learning strategy

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Finding multiple Nash equilibria via machine learning-supported Grobner bases

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期284卷 1178-1189页

作者： Loschenbrand, Markus Norwegian Univ Sci & Technol Dept Elect Power Engn OS Bragstads Plass 2E N-7034 Trondheim Norway

This paper demonstrates a new approach to yield all potential equilibria in a system with multiple actors by computing the Grobner basis of the original problem. Further, it discusses the advantages of choosing this method over traditional numerical approaches. In addition, it provides a concept that applies machine learning, specifically evolution strategies, to approximate utility functions. This is done in order to remove the requirement for solving the dual problem of the system and thus allows to scale the equilibrium computation method to larger, more complex problem setups. This is demonstrated by a novel case study on non-convex players in form of hydropower generators operating simultaneously on spot and reserve markets whilst considering startups and shutdowns, periodical inflows and non-linear water conversion head effects. The model is further extended by considering uncertainty under unknown distributions and solved for a large-scale example in form of a multi-area network resembling the Scandinavian power system. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Game theory OR in energy evolutionary computations Non-linear programming

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evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons

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SWARM AND evolutionary COMPUTATION 2019年 51卷 100605-000页

作者： Zhou, Jiajun Gao, Liang Yao, Xifan Zhang, Chunjiang Chan, Felix T. S. Lin, Yingzi Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Kowloon Hong Kong Peoples R China Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

关键词： Service composition Discrete optimization evolutionary computations Many-objective optimization Comparative analysis

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Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期181卷 1620-1629页

作者： Neumann, Frank Univ Kiel Inst Informat D-24098 Kiel Germany

evolutionary algorithms are applied to problems that are not well understood as well as to problems in combinatorial optimization. The analysis of these search heuristics has been started for some well-known polynomial solvable problems. Such analyses are starting points for the analysis of evolutionary algorithms on difficult problems. We present the first runtime analysis of a multi-objective evolutionary algorithm on a NP-hard problem. The subject of our analysis is the multi-objective minimum spanning tree problem for which we give upper bounds on the expected time until a simple evolutionary algorithm has produced a population including for each extremal point of the Pareto front a corresponding spanning tree. These points are of particular interest as they give a 2-approximation of the Pareto front. We show that in expected pseudopolynomial time a population is produced that includes for each extremal point a corresponding spanning tree. (C) 2006 Elsevier B.V. All rights reserved.

关键词： evolutionary computations multi-objective minimum spanning trees expected optimization time

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A population-based fast algorithm for a billion-dimensional resource allocation problem with integer variables

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期261卷 460-474页

作者： Deb, Kalyanmoy Myburgh, Christie Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Maptek Pty Ltd Level 2190 Aberdeen St Northbridge WA 6003 Australia

Among various complexities affecting the performance of an optimization algorithm, the search space dimension is a major factor. Another key complexity is the discreteness of the search space. When these two complexities are present in a single problem, optimization algorithms have been demonstrated to be inefficient, even in linear programming (LP) problems. In this paper, we consider a specific resource allocation problem constituting to an integer linear programming (ILP) problem which, although comes from a specific industry, is similar to other practical resource allocation and assignment problems. Based on a population based optimization approach, we present a computationally fast method to arrive at a near-optimal solution. Compared to two popular softwares (glpk, Makhorin, 2012 and CPLEX, Gay, 2015), which are not able to handle around 300 and 2000 integer variables while continuing to run for hours, respectively, our proposed method is able to find a near-optimal solution in less than second on the same computer. Moreover, the main highlight of this study is to propose a customized population based optimization algorithm that scales in almost a linear computational complexity in handling 50,000 to one billion (10) variables. We believe that this is the first time such a large-sized real-world constrained problem is ever handled using any optimization algorithm. We perform sensitivity studies of our method for different problem parameters and these studies clearly demonstrate the reasons for such a fast and scale-up application of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Population-based optimization algorithm Large-scale optimization Resource allocation problem Billion variables

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evolutionary optimization of transition probability matrices for credit decision-making

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期200卷 557-567页

作者： Zhang, Jingqiao Avasarala, Viswanath Subbu, Raj GE Global Res Ctr Niskayuna NY 12309 USA

Statistical transition probability matrices (TPMs), which indicate the likelihood of obligor credit state migration over a certain time horizon, have been used in various credit decision-making applications. A standard approach of calculating TPMs is to form a one-year empirical TPM and then project it into the future based on Markovian and time-homogeneity assumptions. However, the one-year empirical TPM calculated from historical data generally does not satisfy desired properties. We propose an alternative methodology by formulating the problem as a constrained optimization problem requiring satisfaction of all the desired properties and minimization of the discrepancy between predicted multi-year TPMs and empirical evidence. The problem is high-dimensional, non-convex, and non-separable, and is not effectively solved by nonlinear programming methods. To address the difficulty, we investigated evolutionary algorithms (EAs) and problem representation schemas. A self-adaptive differential evolution algorithm JADE, together with a new representation schema that automates constraint satisfaction, is shown to be the most effective technique. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Risk management Finance evolutionary computations Constraints satisfaction Decision support systems

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Minimum Cost Perturbed Multi-impulsive Maneuver Methodology to Accomplish an Optimal Deployment Scheduling for a Satellite Constellation

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JOURNAL OF THE ASTRONAUTICAL SCIENCES 2023年第3期70卷 1-31页

作者： Bakhtiari, Majid Abbasali, Ehsan Daneshjoo, Kamran Iran Univ Sci & Technol Sch Adv Technol Tehran 16844 Iran Univ Tehran Fac New Sci & Technol FNST Aerosp Grp North Kargar St Tehran Iran Iran Univ Sci & Technol Sch Mech Engn Tehran Iran

The optimal design of a satellite constellation is a highly constrained, multidisciplinary problem. The satellite constellation requires a flexible optimal deployment algorithm that can consider constraints and perturbations. The mission is cost-effective if there is no need to redevelop the deployment algorithm by changing the number of satellites. Therefore, the main purpose of this paper is to develop an optimal flexible algorithm to deploy m identical satellites to the desired satellite constellation. For this purpose, the Intelligent Optimal Satellite Constellation Deployment (IOSCD) algorithm is proposed. This algorithm's intelligence comes from identifying the feasible scenarios and, regarding the mission requirements such as time synchronization and collision avoidance constraints, is performed via an evolutionary optimization algorithm. The optimization algorithm plays two coupled roles in the deployment mission scheduling: selecting the best feasible deployment scenario and time planning for simulations maneuvers. Developing a proper maneuver is also performed in this paper. For this purpose, a proposed methodology called Multi-Impulsive maneuver combined with Lambert Targeting Problem Correction (MILTPC) is introduced. The LTPC is established in the last maneuver to make the transfer orbit tangential to the final orbit and consider orbital perturbations to reduce fuel consumption. The optimization algorithm is applied to IOSCD and MILTPC simultaneously to achieve the best scenario in terms of fuel consumption. The four meta-heuristic optimization algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and the hybrid IWO/PSO are examined once only on MILTPC and once on constellation deployment missions to select the best one for the present investigation.

关键词： Optimal deployment strategy Satellite constellation Shooting method Multi-impulsive maneuver evolutionary computations

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Resource allocation model and double-sphere crowding distance for evolutionary multi-objective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第1期234卷 197-208页

作者： Lei, Yu Gong, Maoguo Zhang, Jun Li, Wei Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Provinc Peoples R China

Convergence speed and diversity of nondominated solutions are two important performance indicators for Multi-Objective evolutionary Algorithms (MOEAs). In this paper, we propose a Resource Allocation (RA) model based on Game Theory to accelerate the convergence speed of MOEAs, and a novel Double-Sphere Crowding Distance (DSCD) measure to improve the diversity of nondominated solutions. The mechanism of RA model is that the individuals in each group cooperate with each other to get maximum benefits for their group, and then individuals in the same group compete for private interests. The DSCD measure uses hyper-spheres consisting of nearest neighbors to estimate the crowding degree. Experimental results on convergence speed and diversity of nondominated solutions for benchmark problems and a real-world problem show the efficiency of these two proposed techniques. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multi-objective programming evolutionary computations Resource allocation Crowding distance

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An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics

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APPLIED MATHEMATICS AND COMPUTATION 2015年 270卷 25-43页

作者： Mavrotas, George Florios, Kostas Figueira, Jose Rui Natl Tech Univ Athens Sch Chem Engn Lab Ind & Energy Econ GR-10682 Athens Greece Athens Univ Econ & Business Dept Stat Athens Greece Univ Nova Lisboa Inst Super Tecn CEG IST P-1200 Lisbon Portugal

In this paper we propose an improved version of a core based algorithm for the multi-objective extension of one of the most well-known combinatorial optimization problems, the multidimensional knapsack problem. The original algorithm was designed only for bi-objective problems combining an extension of the core concept and a branch-and-bound algorithm. It is a deterministic algorithm and the core concept exploits the "divide and conquer" solution strategy which proves very effective in such problems. The new version is not limited to bi-objective problems;it can effectively handle problems with more than two objective functions and it has features that greatly accelerate the solution process. Namely, these features are the use of a heuristic based on the Dantzig bound in the fathoming process and the better housekeeping of the incumbent list through filtering of solutions. The key parameters of the new algorithm are (a) the size of the core and (b) the number of provided cores. Varying these parameters the user can easily tune the size of the obtained Pareto set. A comparison with evolutionary algorithms, like NSGA II, SPEA2 and MOEA/D, has been made according to run time and the most widely used metrics (set coverage, set convergence etc). Our new version performs better than the most popular evolutionary algorithms and it is comparable to very recent state-of-the-art metaheuristics, like Multi-objective Memetic Algorithm based on Decomposition (MOMAD), for multi-objective programming. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Combinatorial optimization Branch-and-bound evolutionary computations Metaheuristics Multi-objective programming Multi-dimensional knapsack problems

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Intelligent Systems in Optimizing Reservoir Operation Policy: A Review

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WATER RESOURCES MANAGEMENT 2013年第9期27卷 3387-3407页

作者： Hossain, Md. Shabbir El-shafie, A. Univ Kebangsaan Malaysia Dept Civil & Struct Engn Bangi 43600 Malaysia

The paper presents a survey of several optimization techniques, mainly artificial intelligences (AIs) which have been applied to the reservoir operation modelling whether its single or multi-reservoir system. The reservoir system modeling is essential for any nations and the optimal use of it is always asked. The main objective of this review article is to discuss the potentiality of the evolutionary algorithms (EAs) and the ability to integrate with other techniques which can provide the best results. Also the formulation of these types of application has described on the ground of a well known benchmark problem regarding this field. The traditional algorithms got some drawbacks. The study provides a complete understanding to the EA users about next generation optimal search procedure and help to overcome the drawbacks. Though the background of application number of swarm intelligences is less comparatively than the genetic algorithm (GA), it provides a great scope for the researcher for further development. Also comparative results with other popular methods (such as, linear programming, stochastic dynamic programming) are discussed on the basis of past research results. Conclusions and suggestive remarks are made for the help of researchers and the reservoir decision makers as well.

关键词： Reservoir operation Genetic algorithm evolutionary computations Swarm intelligence

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