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An exploratory research of elitist probability schema and its applications in evolutionary algorithms

APPLIED INTELLIGENCE 2014年第4期40卷 695-709页

作者： Zhang, Hong-Guang Liu, Yuan-An Tang, Bi-Hua Liu, Kai-Ming Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100088 Peoples R China

An important problem in the study of evolutionary algorithms is how to continuously predict promising solutions while simultaneously escaping from local optima. In this paper, we propose an elitist probability schema (EPS) for the first time, to the best of our knowledge. Our schema is an index of binary strings that expresses the similarity of an elitist population at every string position. EPS expresses the accumulative effect of fitness selection with respect to the coding similarity of the population. For each generation, EPS can quantify the coding similarity of the population objectively and quickly. One of our key innovations is that EPS can continuously predict promising solutions while simultaneously escaping from local optima in most cases. To demonstrate the abilities of the EPS, we designed an elitist probability schema genetic algorithm and an elitist probability schema compact genetic algorithm. These algorithms are estimations of distribution algorithms (EDAs). We provided a fair comparison with the persistent elitist compact genetic algorithm (PeCGA), quantum-inspired evolutionary algorithm (QEA), and particle swarm optimization (PSO) for the 0-1 knapsack problem. The proposed algorithms converged quicker than PeCGA, QEA, and PSO, especially for the large knapsack problem. Furthermore, the computation time of the proposed algorithms was less than some EDAs that are based on building explicit probability models, and was approximately the same as QEA and PSO. This is acceptable for evolutionary algorithms, and satisfactory for EDAs. The proposed algorithms are successful with respect to convergence performance and computation time, which implies that EPS is satisfactory.

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A multi-cycled sequential memetic computing approach for constrained optimisation

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INFORMATION SCIENCES 2016年 340卷 175-190页

作者： Sun, Jianyong Garibaldi, Jonathan M. Zhang, Yongquan Al-Shawabkeh, Abdallah Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England China Jiliang Univ Dept Appl Math Hangzhou 310018 Zhejiang Peoples R China Univ Greenwich Fac Sci & Engn Chatham ME4 4TB Kent England

In this paper, we propose a multi-cycled sequential memetic computing structure for constrained optimisation. The structure is composed of multiple evolutionary cycles. At each cycle, an evolutionary algorithm is considered as an operator, and connects with a local optimiser. This structure enables the learning of useful knowledge from previous cycles and the transfer of the knowledge to facilitate search in latter cycles. Specifically, we propose to apply an estimation of distribution algorithm (EDA) to explore the search space until convergence at each cycle. A local optimiser, called DONLP2, is then applied to improve the best solution found by the EDA. New cycle starts after the local improvement if the computation budget has not been exceeded. In the developed EDA, an adaptive fully-factorized multivariate probability model is proposed. A learning mechanism, implemented as the guided mutation operator, is adopted to learn useful knowledge from previous cycles. The developed algorithm was experimentally studied on the benchmark problems in the CEC 2006 and 2010 competition. Experimental studies have shown that the developed probability model exhibits excellent exploration capability and the learning mechanism can significantly improve the search efficiency under certain conditions. The comparison against some well-known algorithms showed the superiority of the developed algorithm in terms of the consumed fitness evaluations and the solution quality. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Multi-cycled sequential memetic computing approach estimation of distribution algorithm Constrained optimisation

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Multi-mode resource leveling in projects with mode-dependent generalized precedence relations

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 97卷 193-204页

作者： Li, Hongbo Dong, Xuebing Shanghai Univ Sch Management Shangda Rd 99 Shanghai 200444 Peoples R China

In real-life project management, resource leveling is an important technique to ensure the effective use of resources, in which activities (a) can often be executed in alternative modes and (b) are constrained by precedence relations with minimum and maximum time lags that can be modeled using generalized precedence relations (GPRs). In addition, the values of the time lags tend to depend on activity modes. The resource leveling problem with multiple modes and mode-dependent GPRs (MRLP-GPR) is a generalization of the classic NP-hard resource leveling problem. To our knowledge, no literature exists regarding the MRLP-GPR. We propose several heuristics for the MRLP-GPR built upon two solution approaches: (a) a steepest descent algorithm and a fast descent algorithm that are based on a decomposition approach and (b) a hybrid estimation of distribution algorithm (EDA), which is based on an integration approach. Extensive computational experiments on a large number of benchmark instances are conducted to evaluate the proposed heuristics. A comparison of the results shows that our EDA outperforms or is competitive with three baseline heuristics (a random search algorithm and two variants of a genetic algorithm that is the best-performing metaheuristic for the single-mode resource leveling problem with GPRs). Our results can serve as a benchmark for future research. Our model and solution algorithms provide an automatic tool for the project manager's multi-mode resource leveling decision-making. (C) 2017 Published by Elsevier Ltd.

关键词： Project management Project scheduling Resource leveling Generalized precedence relations Multi-mode case estimation of distribution algorithm

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Simulation optimization for the vehicle routing problem with time windows using a Bayesian network as a probability model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期85卷 2505-2523页

作者： Perez-Rodriguez, Ricardo Hernandez-Aguirre, Arturo CIMAT AC Callejon Jalisco S-N Guanajuato 36240 Mexico

The main purpose of the vehicle routing problem (VRP) is to deliver a set of customers with known demands on minimum-travel routes and terminating at the same depot. The vehicle routing problem with time windows (VRPTW) requires the delivery made in a specific time window for every customer and returning to the depot before a due time. Contrary to current research, an estimation of distribution-algorithm-based approach coupled with a simulation model is proposed and developed to solve the problem and implement the solution. The approach mentioned makes use of a Bayesian network as a probability model to describe the distribution of the solution space. Furthermore, the approach taken in this study combines the key advantages of both estimation of distribution algorithms (EDA) and simulation. The simulation is used to model the VRPTW environment, while the EDA is used to guide the overall search process to identify the best performing ones. Solomon's (Oper Res 35:254-265, 1987) instances served as input and test parameters in order to show that there exists a relationship and interaction between vertices and positions on the sequence of the VRPTW solution. A better position for each vertex on the sequence can be estimated through a Bayesian network. Experimental results show that the EDA performance was better in 70 % of the cases, as average, for the number of vehicles used in all the trails with respect the other algorithms proposed as a benchmark for comparison with the EDA scheme.

关键词： estimation of distribution algorithm Vehicle routing problem Bayesian network Simulation optimization

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Online Ecological Energy Management for Plug-In HEVs Using Optimal SOC Prediction and Stochastic Optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 10885-10895页

作者： Lim, Hansang Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea

Optimizing power demand and distribution in real time is the primary challenge in the energy management of plug-in hybrid electric vehicles. To address this challenge, this study proposes an online ecological energy management strategy using the optimal state of charge (SOC) prediction and stochastic optimization. Future energy-consumption features at each location are computed from the traffic and route information of the entire remaining trip and the optimal SOC value at the next location is predicted from the computed features using a recurrent neural network. By considering the predicted optimal SOC and average speed as references, the speed and SOC values at the next location are optimized using a modified estimation of distribution algorithm (EDA). The process of generating initial and new populations in EDA is modified for fast and reliable convergence. The optimal SOC prediction considering the energy consumption for the entire remaining trip from the current to final locations ensures long-term optimality. Stochastic aspects of the modified EDA can compensate for potential neural-network errors. The prediction and optimization of driving only up to the next location enables to adapt to the changes in traffic conditions. Moreover, the energy efficiency is further improved by optimizing power demand based on the characteristics of the drivetrain, as well as traffic and route conditions. Because the traffic and route information are provided on a location basis, management is performed in a distance domain. The proposed management strategy is evaluated in diverse driving scenarios, and it exhibits good performance, comparable to that of the offline optimization.

关键词： State of charge Optimization Batteries Real-time systems Biological system modeling Motors Power distribution estimation of distribution algorithm feed-forward neural network long-term optimality online ecological energy management optimal speed and state-of-charge recurrent neural network

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A hybrid EDA for load balancing in multicast with network coding

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APPLIED SOFT COMPUTING 2017年 59卷 363-377页

作者： Xing, Huanlai Li, Saifei Cui, Yunhe Yan, Lianshan Pan, Wei Qu, Rong Southwest Jiaotong Univ Sch Informat Sci & Technol Room 9439Teaching Bldg 9Xipu Campus Chengdu 611756 Sichuan Peoples R China Univ Nottingham Sch Comp Sci ASAP Grp Nottingham England

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Load balancing Multicast Network coding Population based incremental learning

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An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 117卷 498-509页

作者： Wu, Chu-ge Li, Wei Wang, Ling Zomaya, Albert Y. Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW 2006 Australia

With rapid development of the Internet of Things (IoT), a vast amount of raw data produced by IoT devices needs to be processed promptly. Compared to cloud computing, fog computing nodes are closer to data resource for decreasing the end-to-end transmission latency. Considering the limited resource of IoT devices, offloading computationally-intensive tasks to the servers with high computing capability is essential in the IoT-fog-cloud system to complete those tasks on time. In this work, we propose a fuzzy logical offloading strategy for IoT applications characterized by uncertain parameters to optimize both agreement index and robustness. A multi-objective estimation of distribution algorithm (EDA) is designed to learn and optimize the fuzzy offloading strategy from a diversity of the applications. The algorithm partitions applications into independent clusters, so that each cluster can be allocated to the corresponding tier for further processing. Thus, system resources are saved by making scheduling decisions in a reduced search space. Simulation studies on benchmark problems and real-world cases are carried out to verify the efficiency of our proposed algorithm. Pareto sets produced by our algorithm outperformed classic heuristic solutions for 88.3% benchmark cases and dominated Pareto sets of two state-of-art multi-objective algorithms for 92.7% and 94.4% cases correspondingly. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Edge computing Internet of Things Evolutionary computation estimation of distribution algorithm Fuzzy scheduling Agreement index Robustness

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Optimal decoding and minimal length for the non-unique oligonucleotide probe selection problem

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NEUROCOMPUTING 2010年第13-15期73卷 2407-2418页

作者： Ghoraie, Laleh Soltan Gras, Robin Wang, Lili Ngom, Alioune Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

One of the applications of DNA microarrays is recognizing the presence or absence of different biological components (targets) in a sample. Hence, the quality of the microarrays design which includes selecting short Oligonucleotide sequences (probes) to be affixed on the surface of the microarray becomes a major issue. A good design is the one that contains the minimum possible number of probes while having an acceptable ability in identifying the targets existing in the sample. This paper focuses on the problem of computing the minimal set of probes which is able to identify each target of a sample, referred to as non-unique oligonucleotide probe selection. We present the application of an estimation of distribution algorithm (EDA) named Bayesian optimization algorithm (BOA) to this problem, for the first time. The proposed approach considers integration of BOA and one simple heuristic introduced for the non-unique probe selection problem. The results provided by this approach compare favorably with the state-of-the-art methods in the single target case. While most of the recent research works on this problem has been focusing on the single target case only, we present the application of our method in integration with decoding approach in a multiobjective optimization framework for solving the problem in the case of multiple targets. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

关键词： Microarray Probe selection Target estimation of distribution algorithm Bayesian optimization algorithm Heuristic Multiobjective optimization Decoding Markov chain Monte Carlo

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A hybrid multi-objective PSO-EDA algorithm for reservoir flood control operation

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APPLIED SOFT COMPUTING 2015年 34卷 526-538页

作者： Luo, Jungang Qi, Yutao Xie, Jiancang Zhang, Xiao Xian Univ Technol State Key Lab Base Ecohydraul Engn Arid Area Xian 710048 Shaanxi Peoples R China Xian Univ Technol Inst Water Resources & Hydroelect Engn Xian 710048 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

Reservoir flood control operation (RECO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RECO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RECO (MO-RECO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO-EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RECO problem. MO-PSO-EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RECO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO-EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO-EDA is suitable for solving MO-RECO problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Hybrid algorithm Particle swarm optimization algorithm estimation of distribution algorithm Reservoir flood control operation

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Balancing exploration and exploitation in multiobjective evolutionary optimization

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INFORMATION SCIENCES 2019年 497卷 129-148页

作者： Zhang, Hu Sun, Jianyong Liu, Tonglin Zhang, Ke Zhang, Qingfu Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

The tradeoff between exploration and exploitation is critical to the performance of an evolutionary algorithm. Different levels of exploration-exploitation tradeoff are required at different evolutionary stages for achieving a satisfactory performance of an evolutionary algorithm. In this paper, we propose a novel survival analysis method to intelligently guide the maintenance of the exploration-exploitation tradeoff in multiobjective evolutionary algorithms. The survival analysis stems from a deep understanding of the evolutionary search procedure. Through survival analysis, an indicator is derived, which is used to guide the adoption of appropriate recombination operators, based on the assumption that the roles of these operators in terms of their capabilities on exploration-exploitation can be asserted. In the developed algorithm, a differential evolution recombination operator and a new sampling strategy are hybridized. Empirical comparison with five well-known multiobjective evolutionary algorithms on a number of test instances with complex Pareto sets and Pareto fronts indicates the effectiveness and superiority of the developed algorithm in terms of commonly-used performance metrics on these test instances. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Multiobjective optimisation Exploration and exploitation estimation of distribution algorithm Survival analysis

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