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objective space division-based hybrid evolutionary algorithm for handing overlapping solutions in combinatorial problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第4期19卷 3369-3401页

作者： Gonzalez, Begona Rossit, Daniel A. Mendez, Maximo Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria Spain Univ Nacl Sur Engn Dept INMABB UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Sur Engn Dept ILESS UNS CONICET Bahia Blanca Buenos Aires Argentina

Overlapping solutions occur when more than one solution in the space of decisions maps to the same solution in the space of objectives. This situation threatens the exploration capacity of multi objective evolutionary algorithms (MOEAs), preventing them from having a good diversity in their population. The influence of overlapping solutions is intensified on multi-objective combinatorial problems with a low number of objectives. This paper presents a hybrid MOEA for handling overlapping solutions that combines the classic NSGA-II with a strategy based on objective Space Division (OSD). Basically, in each generation of the algorithm, the objective space is divided into several regions using the nadir solution calculated from the current generation solutions. Furthermore, the solutions in each region are classified into non-dominated fronts using different optimization strategies in each of them. This significantly enhances the achieved diversity of the approximate front of non-dominated solutions. The proposed algorithm (called NSGA-II/OSD) is tested on a classic Operations Research problem: the multi-objective Knapsack Problem (0-1 MOKP) with two objectives. Classic NSGA-II, MOEA/D and Global WASF-GA are used to compare the performance of NSGA-II/OSD. In the case of MOEA/D two different versions are implemented, each of them with a different strategy for specifying the reference point. These MOEA/D reference point strategies are thoroughly studied and new insights are provided. This paper analyses in depth the impact of overlapping solutions on MOEAs, studying the number of overlapping solutions, the number of solution repairs, the hypervolume metric, the attainment surfaces and the approximation to the real Pareto front, for different sizes of 0-1 MOKPs with two objectives. The proposed method offers very good performance when compared to the classic NSGA-II, MOEA/D and Global WASF-GA algorithms, all of them well-known in the literature.

关键词： objective space division overlapping solutions multi-objective evolutionary algorithms multi-objective combinatorial optimization problems bi-objective knapsack problem

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A Novel multi-objective Optimization Based evolutionary Algorithm for Optimize the Services of Internet of Everything

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IEEE ACCESS 2022年 10卷 106798-106811页

作者： Singh, Shailendra Pratap Dhiman, Gaurav Viriyasitavat, Wattana Kautish, Sandeep Galgotias Univ Sch Comp Sci & Engn Greater Noida 203201 India Lebanese Amer Univ Dept Elect & Comp Engn Byblos 1701 Lebanon Chandigarh Univ Ctr Res & Dev Dept Comp Sci & Engn Gharuan 140413 Mohali India Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 Uttar Pradesh India Univ Int Iberoamer Dept Project Management Campeche 24560 Mexico Chulalongkorn Univ Fac Commerce & Accountancy Dept Stat Business Informat Technol Div Bangkok 10330 Thailand Lord Buddha Educ Fdn LBEF Campus Kathmandu 44600 Nepal

In the new era, the Internet of Everything (IoE) provides distributed services like data, processes, people, and things, etc. The services to the connected IoE significantly increase the time of service, workload, energy consumption, and delay. These objectives conflict with each other. To address the issue, a novel multi-objective based evolutionary algorithm is proposed. In the proposed method, a new rapid mutation operator is incorporated with multi-objective differential evolution (MODE) to overcome the stagnation of the local optimum. The proposed method to maintain the diversity and enhance the convergence speed of the existing MODE algorithm is described. The proposed method provides more diversity and convergence speed for choosing better candidate solutions. The addition of the proposed method is evaluated with the application of IoE services. We have designed the two objective and three objective-based IoE services scenarios. Furthermore, the proposed method optimizes services like service cost, service delay, and the lifetime of sensors. It is interesting to observe that the proposed approach better performs the most recent state-of-the-art multiobjective evolutionary algorithms.

关键词： Internet of Things Optimization evolutionary computation Delays Costs Heuristic algorithms Cloud computing Adaptation differential evolution multi-objective evolutionary algorithms Internet of Everything

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Solving multi-objective truss structural optimization problems considering natural frequencies of vibration and automatic member grouping

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evolutionary INTELLIGENCE 2024年第2期17卷 653-678页

作者： Carvalho, Erica C. R. Carvalho, Jose Pedro G. Bernardino, Heder S. Lemonge, Afonso C. C. Hallak, Patricia H. Vargas, Denis E. C. Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil Univ Fed Rio de Janeiro Civil Engn Program COPPE Rio De Janeiro Brazil Univ Fed Juiz de Fora Inst Exact Sci Dept Comp Sci Juiz De Fora Brazil Univ Fed Juiz de Fora Sch Engn Dept Appl & Computat Mech Juiz De Fora Brazil Fed Ctr Technol Educ Minas Gerais Dept Math Belo Horizonte Brazil

Minimizing the mass of a structure and maximizing its first natural frequency of vibration are conflicting objectives of real interest in structural design. To avoid problems with resonance, which can lead to their collapse, structures can be designed by making the first natural frequency of vibration high. Furthermore, it is crucial to stay away from excitation frequencies. Here we formulate and solve multi-objective structural optimization problems of trusses with these conflicting objectives. This type of problem is uncommon in the literature, since the natural frequencies of vibration are generally set as constraints rather than as objective functions. The generalized differential evolution 3 (GDE3), the nondominated sorting genetic algorithm II (NSGA-II), decision space-based niching (DN-NSGA-II), the competitive mechanism-based multi-objective particle swarm optimizer (CMOPSO), and the MOPSO with multiple search strategies (MMOPSO) are the algorithms used in this paper. Additionally, cardinality constraints are used to manage the difficulty of discovering the best member grouping, which is very effective in addressing the problems analyzed in this paper. The experiments refer to the 10-, 25-, 72-, and 200-bar trusses. Each involves two analyses, taking into account the performance indicators and the use of a multi tournament decision (MTD) method to extract the desired solutions. Furthermore, the design variables of each extracted solution, including its optimized topology, are provided.

关键词： multi-objective truss structural optimization multi-objective evolutionary algorithms Natural frequencies of vibration Cardinality constraints Automatic member grouping

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A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration

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IEEE ACCESS 2023年 11卷 62799-62819页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Touti, Ezzeddine Ahmed, Zahoor Alsalman, Osamah Kim, Yun-Su Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Tunis Elect Engn Dept Lab Ind Syst Engn & Renewable Energies ENSIT Tunis 1008 Tunisia Balochistan Univ Engn & Technol Khuzdar Dept Elect Engn Khuzdar 89100 Pakistan King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

With urban and rural infrastructure development, the power system is being forced to operate at or near its full capacity. This paper proposes four new methodologies to find the solution to the optimal reactive power dispatch (ORPD) problem, considering the capabilities of modern DFIG-based WTs and VSI-based solar PV. The proposed formulation considers the techno-economic objective functions, specifically the minimization of the active and reactive power cost and the maximization of reactive power reserve. This leads to an effective solution to the probabilistic multi-objective ORPD (PMO-ORPD) problem, especially in the context of modern wind farms (WFs) and solar PV. The proposed formulations are necessary for effectively managing power systems with renewable energy sources and contribute to developing efficient and sustainable power systems. Additionally, this study employs probabilistic mathematical modeling that incorporates Weibull, lognormal, and normal probability distribution functions (PDFs) to represent uncertainties in the wind, solar, and load demand. Monte-Carlo simulation (MCS) is employed to generate probabilistic scenarios, allowing for a comprehensive analysis of the PMO-ORPD problem. A new two-phase (ToP) multi-objective evolutionary algorithm is proposed, which incorporates the superiority of feasibility constraints to effectively solve the probabilistic multi-objective optimal reactive power dispatch (PMO-ORPD) problem. From the analysis and comparison of simulation results, it has been observed that the proposed algorithm effectively solves the deterministic and PMO-ORPD problems.

关键词： Constraint domination principle multi-objective evolutionary algorithms optimal reactive power dispatch reactive power reserve renewable energy sources

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multi-objective evolutionary Neural Architecture Search with Weight-Sharing Supernet

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6143页

作者： Liang, Junchao Zhu, Ke Li, Yuan Li, Yun Gong, Yuejiao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm's exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed

关键词： deep learning neural architecture search multi-objective evolutionary algorithms image classification

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Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints 18th

Biased Pareto Optimization for Subset Selection with Dynamic...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Liu, Dan-Xuan Qian, Chao Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9783031700842;9783031700859

Subset selection with cost constraints aims to select a subset from a ground set to maximize a monotone objective function without exceeding a given budget, which has various applications such as influence maximization and maximum coverage. In real-world scenarios, the budget, representing available resources, may change over time, which requires that algorithms must adapt quickly to new budgets. However, in this dynamic environment, previous algorithms either lack theoretical guarantees or require a long running time. The state-of-the-art algorithm, POMC, is a Pareto optimization approach designed for static problems, lacking consideration for dynamic problems. In this paper, we propose BPODC, enhancing POMC with biased selection and warm-up strategies tailored for dynamic environments. We focus on the ability of BPODC to leverage existing computational results while adapting to budget changes. We prove that BPODC can maintain the best known (alpha(f) /2)(1 - e-(alpha f))-approximation guarantee when the budget changes. Experiments on influence maximization and maximum coverage show that BPODC adapts more effectively and rapidly to budget changes, with a running time that is less than that of the static greedy algorithm.

关键词： Subset selection Dynamic cost constraints Pareto optimization multi-objective evolutionary algorithms

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Innovization for Route Planning Applied to an Uber Movement Speeds Dataset for Berlin 18th

Innovization for Route Planning Applied to an Uber Movement ...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Roeper, Eva Weise, Jens Steup, Christoph Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany Agentur Innovat Cybersicherheit GmbH Halle Germany Fraunhofer Inst Transportat & Infrastruct Syst IV Dresden Germany

ISBN: (纸本)9783031700842;9783031700859

multi-objective route planning is a prominent but computationally expensive optimisation problem of everyday life. Reusing knowledge from similar route planning problems could enhance the performance and the sustainability of routing algorithms. The goal of this paper is to adapt the concept of innovization to route planning and in this way extract knowledge from Pareto-optimal solutions. As part of the adaptation, we design a multi-objective evolutionary algorithm for routing and introduce a novel local search for routing problems called Perimeter Mutation Local Search. We evaluate our proposed approach on multi-objective time-dependent routing problems to see what knowledge can be gained and whether this knowledge can improve a multi-objective evolutionary algorithm. Our results show that we can extract knowledge using the introduced innovization for route planning. This knowledge is used to improve a multiobjective evolutionary algorithm by reducing computational effort. With only about 40 % of previously necessary function evaluations, we manage to produce similar optimisation results. This is particularly beneficial for mobile applications with limited available computational resources.

关键词： Innovization Time-Dependent Route Planning multi-objective evolutionary algorithms

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An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem

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SOFT COMPUTING 2021年第8期25卷 6527-6538页

作者： Fu, Guo-Zhong Huang, Hong-Zhong Li, Yan-Feng Zhou, Jie Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Sichuan Peoples R China

multi-objective evolutionary algorithms (MOEAs) have been successfully employed to solve many scientific and engineering problems. However, many algorithms perform ill in maintaining diversity and convergence simultaneously. In this paper, we devised a novel operator selection framework based on two collaborative indicators, generational distance (GD) and maximum spread (MS) to improve the diversity while maintaining a good convergence. By calculating the variation of GDs and MSs over the past 7 iterations, an instruction is conveyed to select a proper operator to execute next 7 iterations. This process is repeated until it reaches the maximum iteration. Two operators are embedded in this algorithm which are differential evolution operator (DE/rand/1) and our proposed crow search operator which is deemed to be efficient in explorating the search space. MOEA/D is utilized as the basis framework of our proposed algorithm. Experiments indicate that our proposed algorithm is valid and outperforms other famous algorithms in terms of diversity and convergence. In the end, a particular aeroengine maintenance scheduling problem is solved by our proposed algorithm.

关键词： multi-objective evolutionary algorithms Collaborative indicator-based operator selection Differential evolution Crow search Maintenance scheduling problem

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Topology structure optimization of evolutionary hierarchical fuzzy systems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Zhao, Tao Zhu, Yue Xie, Xiangpeng Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems.

关键词： Fuzzy rule-based systems Hierarchical fuzzy systems Topology structure optimization multi-objective evolutionary algorithms High-dimensional regression problems

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When Is Non-deteriorating Population Update in MOEAs Beneficial? 1

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13th International Conference on evolutionary multi-Criterion Optimization, EMO 2025

作者： Zhang, Qiaozhi Li, Miqing Tang, Ke Yao, Xin The University of Birmingham Birmingham United Kingdom Southern University of Science and Technology Shenzhen China Lingnan University China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Population update in multi-objective evolutionary algorithms (MOEAs) can be seen as an archiving process, namely, updating the population by comparing it with new solutions and deciding which ones to keep and which ones to discard. It thus may be useful for a population update mechanism to have some desirable archiving properties;for example, non-deteriorating, i.e., the population does not accept solutions that are inferior to solutions eliminated in the past. Unfortunately, none of mainstream MOEAs hold such a property. On the other hand, different from the archiving process which focuses on how to choose the best solutions from a set of candidate solutions, population update in MOEAs also cares about where these candidate solutions come from and what they are used for. It has been reported recently that allowing some dominated solutions in the population may help the search jump out of local optima. In this paper, we investigate the above seemingly "contradictory" observations, and aim to answer the question of whether holding the property non-deteriorating is beneficial or detrimental to MOEAs. We examine three representative MOEAs and modify their population update rules to make them non-deteriorating. We find that holding it is not necessarily beneficial for all the algorithms, but is generally useful, particularly for those whose population update mechanism does not hold any other desirable properties (e.g., set-monotone and limit-stable) like NSGA-II. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Archiving Elitism multi-objective evolutionary algorithms Population update

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