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Aligning heterogeneous optimization problems with optimal correspondence assisted affine transformation for evolutionary multi-tasking

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APPLIED SOFT COMPUTING 2023年 136卷

作者： Chen, An Ren, Zhigang Wang, Muyi Su, Shenyu Yun, Jiaqi Wang, Yichuang Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Evolutionary multi-tasking optimization (EMTO) aims to boost the overall efficiency of optimizing multiple tasks by triggering knowledge transfer among them. Unfortunately, it may suffer from negative transfer on heterogeneous composite tasks that have low similarity. Some studies try to learn an intertask alignment transformation based on the paired samples from the involved tasks, but risk a failed alignment with improper pairwise methods. To solve this issue, this study proposes an optimal correspondence assisted affine transformation (OCAT) algorithm. OCAT explicitly constructs a mathematical model for the intertask alignment problem and theoretically deduces its optimal solution in an iterative method. As a result, the sample correspondences that enable the learned transformation to achieve the maximum intertask similarity can be located. Besides, a novel approach to deriving the affine transformation formula is also developed for OCAT. The resulting affine alignment transformation will not impair the knowledge contained in the tasks during the alignment process. By integrating OCAT with the estimation of distribution algorithm, this study finally develops a many-tasking optimization algorithm named MaT-EDA, where the solutions from other tasks are explicitly transferred as the samples for estimating the current distribution model. Extensive simulation studies have indicated that OCAT can significantly enhance the performance of EMTO, and MaT-EDA also achieves impressive many-tasking optimization performance.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Evolutionary multi-tasking Intertask alignment Affine transformation Many-tasking optimization estimation of distribution algorithm

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Manufacturing rescheduling after crisis or disaster-caused supply chain disruption

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期157卷

作者： Bo, Hongguang Chen, Xiao Alison Luo, Qian Wang, Wenpeng Dalian Univ Technol Sch Econ & Management 2 Linggong Rd Dalian Peoples R China Univ New Hampshire Peter T Paul Coll Business & Econ 10 Garrison Ave Durham NH 03824 USA Xian Jiaotong Liverpool Univ Int Business Sch Suzhou 111 RenAi Rd Suzhou Peoples R China

In this paper, we study the problems a repair shop has with rescheduling after major supply disruptions. The repair shop provides repair and maintenance services to its customers. After a major disruption to production, the repair shop faces delays in production and order delivery due to shortages in materials and/or labor, which requires rescheduling of all the unfinished parts. We observe that the finished parts incur high holding costs until the entire order is completed, while any unfinished parts (in the form of raw material or work-in-progress) incur low holding costs until production starts. Moreover, the repair shop incurs a setup cost when switching between different types of parts. Considering these new features, we formulate the rescheduling problem for the repair shop under a coordinated supply chain as an integer program to minimize the total tardiness, setup cost, and holding cost. To solve the model, we propose an innovative two-stage genetic algorithm, which utilizes the estimation of distribution algorithm (EDA) to improve the search process of the optimal solution. We test the performance of this algorithm on a dataset generated from the order data of a heavy machinery maintenance provider. The numerical results show that our model generates solutions that outperform the initial schedule, which was obtained by minimizing holding and setup costs without disruption. In addition, using other closely-related genetic algorithms as benchmarks, we show that our algorithm outperforms the benchmarks without sacrificing the computational time. We also discuss an extension of the main model by considering the recovery of productivity in terms of processing time.

关键词： Production rescheduling Supply chain disruption Two-stage genetic algorithm estimation of distribution algorithm Productivity recovery

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An effective hyper heuristic-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Song, Hong-Bo Yang, You-Hong Lin, Jian Ye, Jing-Xuan Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

In this paper, an effective Hyper Heuristic-based Memetic algorithm (HHMA) is proposed to solve the Distributed Assembly Permutation Flow-shop Scheduling Problem (DAPFSP) with the objective of minimizing the maximum completion time. A novel searching-stage-based solution representation scheme is presented for both improving the search efficiency and maintaining potential solutions. In the global search stage, estimation of distribution algorithm (EDA) is employed as the high level strategy of EDA-based Hyper Heuristic (EDAHH) to find promising product sequences for further exploitation. Based on the newly found knowledge of critical-products, several efficient Low-Level Heuristics (LLHs) are well designed to construct the LLH set so that the powerful exploration ability of the EDAHH can be guaranteed. A simulated-annealing-like type of acceptance criterion is also embedded into each LLH to avoid premature convergence. Then a Critical-Products-based Referenced Local Search (CP-RLS) method is proposed to improve the quality of superior sub-population by operating on the sub-job-sequences derived from the critical products. The benefit of the presented CP-RLS lies in the excellent exploitation ability with substantially reduced computational cost. Finally, performance evaluation and comparison are both carried out on a benchmark set and the results demonstrate the superiority of HHMA over the state-of-the-art algorithms for the DAPFSP.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Distributed assembly flow-shop scheduling estimation of distribution algorithm Hyper heuristic Memetic algorithm Critical-products-based local search

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Hybrid estimation of distribution algorithms for solving a keyboard layout problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2018年第6期35卷 352-367页

作者： Pradeepmon, T. G. Panicker, Vinay V. Sridharan, R. Natl Inst Technol Dept Mech Engn Calicut Kerala India

The use of smartphones and handheld devices in our daily activities has sharply increased. The added features of wireless technology and related applications on these devices make it possible to write emails, notes, and long text. Even though the most commonly used electronic input device is a keyboard, very little work has been dedicated for finding an optimal layout for this device. In this research, the aim is to propose a better layout for the single-finger keyboard in terms of rapid typing. The keyboard layout problem can be formulated as a quadratic assignment problem, which is one of the hardest combinatorial optimization problems. Some well-known literary works in English are chosen for estimating the keying-in-time. A variant of genetic algorithm, namely, the estimation of distribution algorithms is used to find a better layout. The new layout is found to be efficient compared with some of the existing prominent keyboard layouts.

关键词： Single-finger keyboard estimation of distribution algorithm quadratic assignment problem keyboard layout problem population based incremental learning univariate marginal distribution algorithm

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Smart grid planning method based on multi-objective particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2021年第1期13卷 22-31页

作者： Zhang, Jianguang State Grid Shaoxing Power Supply Co Shaoxing 312000 Zhejiang Peoples R China

Smart grid refers to a modern electric energy supply system to tackle a lot of problems in grid management, such as, resource shortage, environment pollution and so on. In this paper, we propose a novel smart grid planning method using multi-objective particle swarm optimisation algorithm. The goal of smart grid plan is to calculate the minimum investment and annual operating costs, when we obtain the planning level of load distribution, substation capacity and power supply area to satisfy the load requirement and optimised substation location. Afterwards, we propose a multi-objective particle swarm optimisation algorithm which integrates the estimation of distribution algorithm. Furthermore, the propose approach divides the particle population into a lot of sub-populations and then build probability models for each population. Finally, experimental results demonstrate that the proposed method can effectively arrange new substation, which is able to make up for deficiencies of current existing substations.

关键词： smart grid planning multi-objective optimisation particle swarm optimisation estimation of distribution algorithm

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Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming

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algorithmS 2024年第5期17卷 180页

作者： Phuntsho, Tshewang Gonsalves, Tad Sophia Univ Dept Informat & Comp Sci Chiyoda Ku Tokyo 1028554 Japan Royal Univ Bhutan Gedu Coll Business Studies POB 21006 Bongo Bhutan

Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The estimation of distribution algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP-DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.

关键词： approximate dynamic programming net present value discounted cash flows Discrete Cuckoo Search estimation of distribution algorithm Ant Colony Optimization Payment at Event Occurrences

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A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization

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COMPLEX & INTELLIGENT SYSTEMS 2021年第5期7卷 2559-2576页

作者： Wu, Yuting Wang, Ling Chen, Jing-fang Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Under the current volatile business environment, the requirement of flexible production is becoming increasingly urgent. As an innovative production mode, seru-system with reconfigurability can overcome the lack of flexibility in traditional flow lines. Compared with pure seru-system, the hybrid seru-system composed of both serus and production lines is more practical for adapting to many production processes. This paper addresses a specific hybrid seru-system scheduling optimization problem (HSSOP), which includes three strongly coupled sub-problems, i.e., hybrid seru formation, seru scheduling and flow line scheduling. To minimize the makespan of the whole hybrid seru-system, we propose an efficient cooperative coevolution algorithm (CCA). To tackle three sub-problems, specific sub-algorithms are designed based on the characteristic of each sub-problem, i.e., a sub-space exploitation algorithm for hybrid seru formation, an estimation of distribution algorithm for seru scheduling, and a first-arrive-first-process heuristic for flow line scheduling. Since three sub-problems are coupled, a cooperation coevolution mechanism is proposed for the integrated algorithm by information sharing. Moreover, a batch reassign rule is designed to overcome the mismatch of partial solutions during cooperative coevolution. To enhance the exploitation ability, problem-specific local search methods are designed and embedded in the CCA. In addition to the investigation about the effect of parameter setting, extensive computational tests and comparisons are carried out which demonstrate the effectiveness and efficiency of the CCA in solving the HSSOP.

关键词： Hybrid seru-system Cooperative coevolution estimation of distribution algorithm Sub-space exploitation Problem-specific local search

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A distribution evolutionary algorithm for the graph coloring problem

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 80卷

作者： Xu, Yongjian Cheng, Huabin Xu, Ning Chen, Yu Xie, Chengwang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Wuchang Shouyi Univ Dept Basic Sci Wuhan 430064 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangzhou 516600 Guangdong Peoples R China

Graph coloring is a challenging combinatorial optimization problem with a wide range of applications. In this paper, a distribution evolutionary algorithm based on a population of probability model (DEA-PPM) is developed to address it efficiently. Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy. By sampling the distribution population, efficient search in the solution space is realized based on a tabu search process. Meanwhile, DEA-PPM introduces an iterative vertex removal strategy to improve the efficiency of k-coloring, and an inherited initialization strategy is implemented to address the chromatic number problem well. The cooperative evolution of the distribution population and the solution population leads to a good balance between exploration and exploitation. Numerical results demonstrate that the DEA-PPM of small population size is competitive to the state-of-the-art metaheuristics.

关键词： distribution evolutionary algorithm Orthogonal exploration Inherited initialization Graph coloring estimation of distribution algorithm

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The maximal covering location problem with accessibility indicators and mobile units

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年第PartB期87卷

作者： Vicencio-Medina, Salvador J. Rios-Solis, Yasmin A. Ibarra-Rojas, Omar Jorge Cid-Garcia, Nestor M. Rios-Solis, Leonardo Tecnol Monterrey Sch Engn & Sci Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico Univ Autonoma Nuevo Leon Fac Ciencias Fisico Matemat San Nicolas De Los Garza 66455 Nuevo Leon Mexico CONACYT Ctr Invest Ciencias Informac Geoespacial Lab Nacl Geointeligencia Aguascalientes Aguascalientes Mexico Univ Edinburgh Inst Bioengn Sch Engn Edinburgh EH9 3BF Scotland Univ Edinburgh Ctr Synthet & Syst Biol SynthSys Edinburgh EH9 3BF Scotland Newcastle Univ Sch Nat & Environm Sci Mol Biol & Biotechnol Newcastle Upon Tyne NE1 7RU England

We study the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units that maximizes the facilities coverage, the accessibility of the zones to the open facilities, and the spatial disaggregation. The main characteristic of our problem is that mobile units can be deployed from open facilities to extend the coverage, accessibility, and opportunities for the inhabitants of the different demand zones. We formulate the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units as a mixed-integer linear programming model. To solve larger instances, we propose a matheuristic (combination of exact and heuristic methods) composed of an estimation of distribution algorithm and a parameterized Maximal Covering Location Problem with Accessibility Indicators and Mobile Units integer model. To test our methodology, we consider the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units model to cover the low-income zones with Severe Acute Respiratory Syndrome Coronavirus 2 patients. Using official databases, we made a set of instances where we considered the poverty index, number of population, locations of hospitals, and Severe Acute Respiratory Syndrome Coronavirus 2 patients. The experimental results show the efficiency of our methodologies. Compared to the case without mobile units, we drastically improve the coverage and accessibility for the inhabitants of the demand zones.

关键词： Facility location problem Matheuristic COVID-19 Mixed-integer linear program Accessibility estimation of distribution algorithm

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Metaheuristics for Optimal Scheduling of Appliances in Energy Efficient Neighbourhoods 1

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20th EPIA Conference on Artificial Intelligence (EPIA)

作者： Alfageme, Amaia Esnaola-Gonzalez, Iker Javier Diez, Francisco Gilabert, Eduardo TEKNIKER Basque Res & Technol Alliance BRTA C Inaki Goenaga 5 Eibar 20600 Spain Univ Basque Country UPV EHU Fac Informat Paseo Manuel Lardizabal 1 Donostia San Sebastian 20018 Spain

ISBN: (数字)9783030862305

ISBN: (纸本)9783030862305;9783030862299

As a consequence of the continuous growth in the worldwide electricity consumption, supplying all customer electrical requests is becoming increasingly difficult for electricity companies. That is why, they encourage their clients to actively manage their own demand, providing several resources such us their Optimal Demand Profile (ODP). This profile provides to users a summary of the demand they should consume during the day. However, this profile needs to be translated into specific control actions first, such as the when each appliance should be used. In this article a comparison of the performance of two metaheuristic optimisation algorithms (Tabu Search and estimation of distribution algorithm (EDA)) and their variants for the calculation of optimal appliance scheduling is presented. Results show that Tabu Search algorithm can reach better feasible solutions at faster execution times than EDA does.

关键词： Appliance scheduling optimisation Optimal Demand Profile Tabu Search estimation of distribution algorithm

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