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A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 1-13页

作者： Fei, Xiaotian Ma, Jun Zhang, Jianhua Zhang, Yong Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Xuzhou Univ Technol Sch Elect & Control Engn Xuzhou 221018 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China

The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system's multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output;then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.

关键词： evolutionary algorithms coal mine integrated energy system multi-task optimization constrained multi-objective optimization

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Comprehensive benefit evaluation of mineral resources development based on dual population constrained multi-objective evolutionary algorithm

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Yue, Xuezhi Cheng, Yating Kang, Lanlan Peng, Hu Zeng, Yuan Jiangxi Univ Sci & Technol Coll Sci Ganzhou 341000 Jiangxi Peoples R China Gannan Univ Sci & Technol Coll Informat Engn Ganzhou 341000 Jiangxi Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Jiangxi Peoples R China Guangdong Commun Polytech Inst Mech & Elect Engn Qingyuan 511500 Guangdong Peoples R China

With the increasing global demand for mineral resources, the comprehensive benefit evaluation of mineral resources development is playing an increasingly important role in the field of mineral resources development. However, there are few attempts to use multi-objective evolutionary algorithms (MOEAs) to study the comprehensive benefit evaluation problem of mineral resources development. To effectively solve this problem, this study established a multi-objective optimization model for comprehensive benefit evaluation of mineral resources development and proposed a dual-population constrained multi-objective evolutionary algorithm (CMOEA). The algorithm uses two populations to organically combine the efficient convergence of the non- dominated sorting genetic algorithm II (NSGA-II) with the advantage of the adaptive geometry estimation based multi-objective evolutionary algorithm (AGE-MOEA) in estimating the shape of the Pareto front, named co-evolution based on non-dominated sorting genetic algorithm and adaptive geometry estimation (C-NSGAAGE). 3 test suites are used to verify the performance of the proposed algorithm. Experimental results show that compared with competing algorithms, C-NSGA-AGE has strong competitiveness inmost test functions. The proposed method is used to solve the model, and five optimal comprehensive benefit evaluation schemes are given for different objectives. Among them, the maximum benefit is obtained when each objective is developed in a balanced way, which reaches 0.837181.

关键词： Comprehensive benefit evaluation constrained multi-objective optimization Dual population Mineral resources

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constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Li, Jianxia Liu, Ruochen Zhang, Xilong Wang, Ruinan Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Many engineering optimization problems require simultaneous optimization of multiple objective functions under certain constraints, which are collectively referred to as constrained multi-objective problems (CMOPs). The crucial issue in solving CMOPs is to balance constraints and objectives. This paper proposes a constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints, termed CORCMO. CORCMO mainly comprises two stages: the learning stage and the evolving stage. The learning stage focuses on analyzing the correlation between each objective and constraints. In the evolving stage, the CMOP is decomposed into M constraint single-objective problems, which are optimized by M subpopulations cooperatively. For each subproblem, the corresponding fitness function, computed based on the correlation, is adopted to guide the evolution. Subsequently, CORCMO employs archive population update strategy to find the optimal solutions of the given CMOP. Experiments conducted on a series of benchmark problems demonstrate that CORCMO is promising to solve CMOPs.

关键词： Correlation constrained multi-objective optimization Decomposition Archive population

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Safety-efficiency integrated assembly: The next-stage adaptive task allocation and planning framework for human-robot collaboration

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 94卷

作者： Zhao, Ruihan Tao, Sichen Li, Pengzhong Tongji Univ Sch Mech Engn Shanghai 200092 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan

Human-robot collaboration (HRC) has sparked a new wave of the resilient, sustainable, and human-centric industrial revolution. In HRC-enabled manufacturing, safety is a constant focus since the traditional physical barriers between robots and humans are removed. Meanwhile, ensuring efficiency has also gained considerable attention with the rise in production demands and market competition. However, the integration and balance between these two critical prerequisites are overlooked in the task allocation and planning phase of the HRC assembly. In this paper, an adaptive task allocation and planning framework is proposed by simultaneously considering the safety and efficiency of HRC assembly. First, an execution time optimization strategy (ETO) is presented. This strategy adaptively adjusts safety paradigms in the spatio-temporal shared workspace. Thereby, the execution time of assembly tasks is well optimized while ensuring human-robot safety. Second, based on ETO and collaborative assembly task requirements, the safety-efficiency integrated assembly planning problem (SEAPP) is proposed and modeled. Third, compared to conventional single-objective HRC assembly planning problems, the solution space of SEAPP is significantly expanded, leading to a substantial increase in optimization complexity. Thus, a novel constrained multi-objective co-evolutionary algorithm, called GDCA, is proposed. By combining the complementary advantages of different evolutionary operators, GDCA enhances the diversity of solutions while ensuring convergence of optimization process. In comparison with variants that rely solely on a single evolutionary strategy, GDCA maintains better and more stable performance, validating the effectiveness of the co-evolutionary process. GDCA is also compared with four state-of-the-art constrained multi-objective optimization algorithms across a variety of SEAPP instances, widely spanning different task scales and durations. Across 20 assembly instances, GDCA s

关键词： Human-robot collaboration Safety-efficiency integration constrained multi-objective optimization Assembly planning

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A constrained multi-objective Differential Evolution Based on Hybrid Mutation Strategy

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-22页

作者： Zeng, Zhiqiang Zhang, Min Zhao, Tao Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China

There are two main challenges in using evolutionary algorithms to solve constrained multi-objective optimization problems. The first is balancing exploration and exploitation capabilities. To address this challenge, we propose a new search algorithm based on four mutation strategies with different characteristics in this study. Two mutation strategies enhance exploration ability, and the other two enhance exploitation ability. The second challenge is balancing the objective functions and constraints. To address this challenge, we propose a random selection of the best individual from two rankings when selecting parent individuals for the mutation operation and an improved constraint handling technique. The first ranking uses Pareto dominance and crowding distance, and the second uses the constrained dominance principle and crowding distance. Finally, we propose a constrained multi-objective differential evolution with hybrid mutation strategy (CMODEHMS). We evaluated it with 51 benchmark test functions and 21 real-world problems to analyze and compare CMODEHMS with eight state-of-the-art constrained multi-objective evolutionary algorithms. The experimental results demonstrated that the proposed CMODEHMS exhibited superior performance compared with competing methods.

关键词： Differential evolution constrained multi-objective optimization Hybrid mutation strategy Constraint handling technique

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KDDCP: Kernel density driven cumulative probability-cooperated framework for constrained muti-objective optimization

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Swarm and Evolutionary Computation 2025年 96卷

作者： Zhao, Shijie Yu, Xin Zhang, Lei Song, Jinling Institute of Intelligence Science and Optimization College of Science Liaoning Technical University Fuxin 123000 China School of Geomatics Liaoning Technical University Fuxin 123000 China Institute for Optimization and Decision Analytics Liaoning Technical University Fuxin 123000 China

In constrained multi-objective problems (CMOPs), striking a balance between population diversity, convergence, and feasibility requirements is a critical challenge for constrained multi-objective evolutionary algorithms (CMOEAs). Overemphasizing feasibility in traditional methods may lead to the search being trapped in local optima, whereas ignoring constraints may result in resource waste. To address these challenges, in this paper, a constrained multi-objective evolutionary algorithm (KDDCP) based on kernel density-driven cooperative cumulative probability is proposed, which can reasonably utilize feasible solutions as well as valuable infeasible solutions in order to approach the real Pareto frontier effectively. Specifically, KDDCP achieves multi-objective search cooperation by constructing the main population, the global auxiliary population and the local auxiliary population: the main population focuses on finding the feasible Pareto front (CPF). Meanwhile, the global auxiliary population approaches the unconstrained Pareto frontier (UPF) under the condition of ignoring constraints, and only runs in the first stage of the algorithm, and stops evolving after convergence, thus avoiding the computational overhead in the subsequent stage. For the local auxiliary population, KDD-RDS and CP-RDB strategies are combined to facilitate detailed local exploration, enhancing both the convergence and diversity of the algorithm. The experimental results show that KDDCP significantly outperforms 13 comparison algorithms on 33 test problems such as MW, DTLZ and DASCMOP as well as on realistic constrained multi-objective optimization problems commonly used in six engineering fields. The results confirm that KDDCP achieves an optimal balance among feasibility, diversity, and convergence under complex constraints. © 2025 Elsevier B.V.

关键词： Auxiliary population mechanism constrained multi-objective optimization Cumulative probability Kernel density driven Knowledge transfer

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A feasible-ratio control technique for constrained optimization

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INFORMATION SCIENCES 2019年 502卷 201-217页

作者： Jiao, Ruwang Zeng, Sanyou Li, Changhe China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

In constrained optimization problems (COPs), a crucial issue is that most constraint handling evolutionary algorithms (EAs) approach the optimum either mainly from feasible regions or mainly from infeasible regions. This may result in bias in search of feasible and infeasible solutions. To address this issue, we propose a feasible-ratio control technique which controls the ratio of feasible solutions in the population. By using the control technique, an EA can maintain the search balance from feasible and infeasible regions. Based on this technique, we propose a constraint-handling EA, named FRC-CEA. It consists of two-stage optimization. In the first stage, an enhanced dynamic multi-objective evolutionary algorithm (DCMOEA) with the feasible-ratio control technique is adopted to handle constraints. In the second stage, a commonly used differential evolution (DE) is used to speed up the convergence. The performance of the proposed method is evaluated and compared with six state-of-the-art constraint-handling algorithms on two sets of benchmark test suites. Experimental results suggest that the proposed method outperforms or is highly competitive against the compared algorithms on most test problems. (C) 2019 Published by Elsevier Inc.

关键词： Evolutionary computation constrained optimization Constraint-handling constrained multi-objective optimization

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Evolutionary constrained Mult-objective optimization Based on Competitive multitasking and Decomposition-Dominance 6

Evolutionary Constrained Mult-objective Optimization Based o...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Xu, Jinyu Wang, Hui Liao, Shitao Liu, Hangyu Wang, Yun Zhou, Xinyu Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9798350377859;9798350377842

During the past two decades, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed for constrained multi-objective problems (CMOPs). However, most of the existing algorithms perform poorly when facing complex CMOPs. Inspired by evolutionary multitasking (EMT) and competitive multitasking (CMT), a novel constrained multi-objective optimization algorithm based on CMT and decomposition- domination (CMT-MOEADD) is developed in this paper. Firstly, in the decomposition stage, two subtasks are designed based on the CMT framework, and a reward mechanism is designed to determine which task is selected to become the main task. One of the sub-tasks uses fuzzy constraint handling techniques to balance the relationship between objectives and constraints, while the other sub-task disregards constraints to find the unconstrained Pareto frontier (UPF). Knowledge transfer between tasks is then relied upon to guide the external profile towards the CPF. Finally, the set of populations and external archives in the dominance stage using the e-MO based constraint handling technique evolve in the EMT framework to accelerate population convergence. In addition, we conduct an experimental study on two test suites from recent years to compare CMT-MOEADD with the state-of-the-art five algorithms. Experimental results demonstrate that our proposed CMT-MOEADD has superior or competitive performance.

关键词： evolutionary algorithm constrained multi-objective optimization multitasking optimization problem competitive multitasking

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A novel competitive constrained dual-archive dual-stage evolutionary algorithm for constrained multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Zhou, Tianwei He, Pengcheng Niu, Ben Yue, Guanghui Wang, Hong Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Shenzhen Univ Great Bay Area Int Inst Innovat Shenzhen 518060 Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Shenzhen 518060 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518060 Peoples R China

In constrained multi-objective optimization problems (CMOPs), the key problem is how to balance convergence and diversity simultaneously and find as many feasible solutions as possible in the reduced search space. To address this issue, a novel competitive constrained dual-archive dual-stage evolutionary algorithm, named c-DADSEA, is proposed with a convergence-driven archive (CA) and a diversity-driven archive (DA). To improve the algorithm's convergence, instead of simply excluding infeasible solutions, an adaptive penalty function is novelly proposed to maintain competitively infeasible solutions in CA. To overcome the difficulty in finding the true Pareto front for the dual-population framework in some problems, a dual-stage framework is introduced to make DA search in different directions at different stages (i.e., Forward and Backward), enhancing the diversity of the algorithm. DA evolves without considering the constraints to explore all regions as much as possible in the Forward stage. While in the Backward stage, DA degenerates with increasing constraints to search the regions around the true Pareto front by using e-constrained method. Moreover, to balance convergence and diversity, different benchmark rankings are designed for the individuals in different archives. The experiments on three benchmark CMOPs test suites demonstrate that c-DADSEA is better than seven other state-of-the-art constrained evolutionary algorithms, especially for the CMOPs with a constrained Pareto front away from the unconstrained Pareto front.

关键词： constrained multi-objective optimization Competitive coevolutionary Adaptive penalty function e-constrained method

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A hybrid constrained multi-objective algorithm for dynamic economic emission dispatch

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International Journal of Electrical Power & Energy Systems 2025年 169卷

作者： Shuqu Qian Huihui He Huihong Wu Philippe Fournier-Viger Hui Li Shoude Huang Anshun University Anshun China No. 1 Middle School of Kaiyang County Kaiyang China Shenzhen University Shenzhen China

Dynamic Economic Emission Dispatch (DEED) problems present a critical multi-objective optimization challenge in power systems, requiring simultaneous minimization of conflicting objectives of costs and emissions while addressing complex operational constraints. This problem becomes particularly intricate when considering the Valve-Point Effect Cost (VPEC) in generation units, which exhibits non-smooth, non-convex characteristics that significantly complicate solution landscapes. To address these challenges, this paper proposes a Hybrid constrained multi-objective Algorithm (HCMOA) incorporating three innovative mechanisms. First, a clonal selection mechanism enhances local exploitation through systematic generation of high-quality non-dominated solutions. Second, a most-crowded neighborhood update strategy combats premature convergence in high-dimensional DEED scenarios while optimizing archive diversity and computational efficiency. Third, a step-by-step repair strategy ensures operational feasibility through systematic output refinement. Comprehensive evaluations on 10-unit and 15-unit systems with varying VPEC configurations demonstrate HCMOA’s superiority over ten state-of-the-art algorithms. Experimental results reveal that HCMOA achieves the Pareto-optimal set with superior spread and distribution uniformity compared to existing approaches. These findings collectively establish HCMOA as an effective solution for practical DEED implementations, particularly in scenarios requiring rigorous consideration of non-convex VPEC characteristics.

关键词： Dynamic Economic Emission Dispatch constrained multi-objective optimization Clonal Selection Principle Most-crowded neighborhood Step-by-step repair

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