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multi-objective Volleyball Premier League algorithm

KNOWLEDGE-BASED SYSTEMS 2020年 196卷

作者： Moghdani, Reza Salimifard, Khodakaram Demir, Emrah Benyettou, Abdelkader Persian Gulf Univ Computat Intelligence & Intelligent Optimizat Res Bushehr Iran Cardiff Univ Cardiff Business Sch Cardiff Wales USTOran Dept Informat BP 1505 El Mnaouer Oran Algeria

This paper proposes a novel optimization algorithm called the multi-objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely multi-objective Particle Swarm Optimization (MOPSO) and multi-objective evolutionary algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Global optimization Pareto solution Engineering design optimization problems

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Hybrid loading situation vehicle routing problem in the context of agricultural harvesting: A reconstructed MOEA/D with parallel populations

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SWARM AND evolutionary COMPUTATION 2024年 91卷

作者： Guo, Xiang Miao, Zhong-Hua Pan, Quan-Ke He, Xuan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China

With the increasing level of agricultural automation, the combination of agriculture and intelligent vehicle technology is propelling the development of smart agriculture. Although this technology has already been applied widely for various agricultural production tasks, inefficient vehicle scheduling still hasn't been resolved satisfactorily. Oriented towards agricultural harvesting scenarios, a hybrid loading situation vehicle routing problem (HLSVRP) model is proposed to minimize total energy consumption and maximum completion time. A reconstructed multi-objective evolutionary algorithm based on decomposition (R-MOEA/D) is developed to solve the problem. Eight solution representations tailored specifically to the problem are introduced by R-MOEA/D, allowing an extensive exploration of the solution space. A modified Clarke & Wright (MCW) heuristic is designed to generate a high-quality initial population. A novel problem-specific parallel population updating mechanism based on the four crossover and two mutation combinations is also provided to improve the exploration ability. A collaborative search strategy is employed to facilitate cooperation among parallel populations. Finally, a series of comparative experiments conducted on various task scales and vehicle scales verify the effectiveness of the proposed algorithmic components and the exceptional performance for solving HLSVRP.

关键词： Agricultural harvesting Hybrid loading situations multi-objective evolutionary algorithm Problem-specific parallel population Collaborative search

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The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies

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ENERGY CONVERSION AND MANAGEMENT 2021年 229卷

作者： He, Yi Guo, Su Zhou, Jianxu Wu, Feng Huang, Jing Pei, Huanjin Hohai Univ Nanjing 210098 Jiangsu Peoples R China

To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology.

关键词： Energy storage Quantitative techno-economic comparison Capacity optimization Operation characteristics multi-objective evolutionary algorithm

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An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷

作者： Niu, Yunyun Kong, Detian Wen, Rong Cao, Zhiguang Xiao, Jianhua China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Singapore Inst Mfg Technol Singapore 138634 Singapore Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of "route failure" caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Vehicle routing problems Stochastic demand Learnable evolution model multi-objective evolutionary algorithm

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A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

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INFORMATION SCIENCES 2024年 665卷

作者： Wang, Qingzhu Li, Tianyang Meng, Fanqi Li, Bin Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Guangdong Atv Acad Performing Arts Sch Informat Engn Dongguan 523710 Peoples R China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome whitebox CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

关键词： Constrained large-scale multi-objective problem multi-objective evolutionary algorithm Decision transfer Lagrange multiplier

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A Type 2 Fuzzy Logic-Based Maintenance Solution for Power System in Renewable Energy Applications

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Xiang, Li Sang, Haitao Qu, Fayi Lingnan Normal Univ Sch Elect & Elect Engn Zhanjiang Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

Power systems are crucial for low-carbon energy applications. Condition maintenance plays a vital role in reducing the maintenance cost of renewable power systems without sacrificing system reliability. This paper proposes a hybrid method to effectively deal with the operational changes and uncertainties of state maintenance within the power system of renewable energy applications. Specifically, a multi-objective evolutionary algorithm is first adopted to maintain key components when only considering system variables and overall performance. During operation, numerous variations in offshore substations are detected from power grids and other equipment, such as continuous aging, weather, load factors, measurement, and human-judgment factors. Then, the advisor implements a system optimization maintenance plan in the substation, which can predict changes in load reliability based on the type 2 fuzzy logic and hidden Markov model technology. The reliability of the load point of each substation would also be obtained. Illustrative results indicate that these serious deteriorations would cause substation for the re-optimization maintenance and optimization activities to meet expected reliability. Through connecting an offshore substation to a medium-sized offshore substation, the uncertainties in condition-based maintenance of renewable energy applications can be well handled.

关键词： renewable energy power system offshore substation multi-objective evolutionary algorithm type 2 fuzzy logic minimum cut set

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multi-objective Sparse Reconstruction With Transfer Learning and Localized Regularization

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IEEE ACCESS 2020年 8卷 184920-184933页

作者： Yan, Bai Zhao, Qi Zhang, J. Andrew Wang, Zhihai Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Technol Sydney Global Big Data Technol Ctr GBDTC Ultimo NSW 2007 Australia Beijing Univ Technol Minist Educ Key Lab Optoelect Technol Beijing 100124 Peoples R China

multi-objective sparse reconstruction methods have shown strong potential in sparse reconstruction. However, most methods are computationally expensive due to the requirement of excessive functional evaluations. Most of these methods adopt arbitrary regularization values for iterative thresholding-based local search, which hardly produces high-precision solutions stably. In this article, we propose a multi-objective sparse reconstruction scheme with novel techniques of transfer learning and localized regularization. Firstly, we design a knowledge transfer operator to reuse the search experience from previously solved homogeneous or heterogeneous sparse reconstruction problems, which can significantly accelerate the convergence and improve the reconstruction quality. Secondly, we develop a localized regularization strategy for iterative thresholding-based local search, which uses systematically designed independent regularization values according to decomposed subproblems. The strategy can lead to improved reconstruction accuracy. Therefore, our proposed scheme is more computationally efficient and accurate, compared to existing multi-objective sparse reconstruction methods. This is validated by extensive experiments on simulated signals and benchmark problems.

关键词： Search problems Image reconstruction Knowledge transfer Iterative methods Convergence evolutionary computation Optimization Sparse reconstruction multi-objective evolutionary algorithm transfer learning regularization

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Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Zhao, Ziyan Jiang, Qi Liu, Shixin Zhou, Mengchu Yang, Xiaochun Guo, Xiwang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China

Energy consumption, production cost, and efficiency are highly concerned by decision makers of energyintensive and high -cost industrial production systems. Intelligent production scheduling is a necessary means to achieve their optimization. This work delves into a novel multi -objective production scheduling problem arising from a steel hot -rolling process, which is a representative energy -intensive and high -cost industrial process. The challenge of the problem involves scheduling customized production jobs subject to intricate process constraints with the goal to minimize three objective functions, i.e., energy consumption, setup cost, and the number of tardy jobs. A mixed integer linear programming model is formulated for the problem. In order to solve it, an improved multi -objective evolutionary algorithm based on decomposition is presented. The algorithm incorporates problem -specific encoding and model -based decoding mechanisms, rendering it wellsuited for addressing the concerned multi -constrained multi -objective optimization problem. The introduced modified Tchebycheff approach mitigates the impact of objective functions with varying value ranges on the algorithm's convergence. Additionally, a Metropolis acceptance criterion is integrated to facilitate the escape from local optimal solutions, enhancing the algorithm's global optimization capability. Numerous experiments are conducted to verify the effectiveness of the improvements and to compare the performance of the presented algorithm against its competitive peers. The results demonstrate its high performance, suggesting its significant potential for its application to steel hot -rolling systems.

关键词： Green manufacturing Mixed integer linear program multi-objective evolutionary algorithm Production scheduling

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Neuroevolutionary diversity policy search for multi-objective reinforcement learning

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INFORMATION SCIENCES 2024年 657卷

作者： Zhou, Dan Du, Jiqing Arai, Sachiyo Chiba Univ Grad Sch Sci & Engn Dept Urban Environm Syst Div Earth & Environm Sci Chiba Japan

Sequential decision-making requires balancing multiple conflicting objectives through multi objective reinforcement learning (MORL). Moreover, decision-makers desire dense solutions that satisfy their requirements and consider the trade-offs between different objectives (Pareto optimal solutions). Most deep reinforcement learning methods focus on single-objective problems or solve multi-objective problems using simple linear combinations, which may oversimplify the underlying problem and lead to suboptimal results. This study proposes a neuroevolutionary diversity policy search approach to address MORL problems. It employs neural networks, each equipped with a buffer for storing recent experiences, representing individuals in a population. The non-dominated sorting method and diversity distance metric are employed in the evolutionary process to select high-quality solutions as teachers. The teachers use gradient-based genetic operators to guide the population to produce high-quality offspring, thereby achieving dense Pareto optimal solutions. Furthermore, we introduce three MORL benchmarks with distinct characteristics: (1) a continuous deep sea treasure with convex and nonconvex Pareto fronts;(2) a multi-objective mountain car with sparse rewards and a discontinuous Pareto front;and (3) a multi-objective HalfCheetah with high-dimensional action-state spaces. The experimental results on the three MORL benchmarks demonstrate the superiority of the proposed algorithm in obtaining dense and high-quality Pareto optimal solutions.

关键词： multi-objective reinforcement learning Pareto front Policy search multi-objective evolutionary algorithm Diversity

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Strategies of multi-objective optimization in drug discovery and development

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EXPERT OPINION ON DRUG DISCOVERY 2011年第9期6卷 871-884页

作者： Nicolotti, Orazio Giangreco, Ilenia Introcaso, Antonellina Leonetti, Francesco Stefanachi, Angela Carotti, Angelo Univ Bari Dipartimento Farmacochim I-70125 Bari Italy

Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properties. multi-objective optimization strategies represent a new approach to capture the occurrence of varying optimal solutions based on trade-offs among the objectives taken into account. In view of this, multi-objective optimization aims to discover a set of satisfactory compromises that may in turn be used to find the global optimal solution by optimizing numerous dependent properties simultaneously. Areas covered: The authors review the potential of multi-objective strategies in a number of fields including: drug library design;substructure mining;the derivation of quantitative structure-activity relationship models;ranking of docking poses. The authors also discuss the potential of multi-objective strategies in controlling competing properties for absorption, distribution, metabolism and elimination/toxicity optimization. Expert opinion: It is very clear to those who work in drug discovery and development that the success of rational drug design is largely dependent on the control of a number of, often conflicting, objectives. Therefore, multi-objective optimization methods, which have recently been introduced to the field of molecular discovery, represent the ultimate frontier in chemoinformatics. The widespread use of these multi-objective techniques has provided new opportunities in medicinal chemistry as seen through its use in a number of applications for chemoinformatics both within academia and the pharmaceutical industry.

关键词： chemoinformatics multi-criterion optimization multi-objective evolutionary algorithm multi-objective optimization

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