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11th World Congress on Intelligent Control and Automation

作者： Sun Fuquan Wang Hongfeng Lu Fuqiang Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781479958252

In recent years, multi-objective optimization problems (MOOPs) have gained a lot of attentions from the community of evolutionary algorithm since many real-world optimization problems would involve multiple objective functions. In this paper, a species-based multi-objective genetic algorithm (SMOGA) that hybridizes a species method, which was initially designed in GA for multi-modal problems, with the algorithm mechanism of NSGA-II, which was one of well-known MOGAs, is proposed for MOOPs. In order to examine the performance of the proposed algorithm, experiments were carried out to investigate the strength and weakness of SMOGA on a series of test MOOPs in comparison with NSGA-II.

关键词： multi-objective optimization problem evolutionary multi-objective optimization genetic algorithm species

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An evolutionary algorithm based on a space-gridding scheme for constrained multi-objective optimization problems 10

An evolutionary algorithm based on a space-gridding scheme f...

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10th International Conference on Computational Intelligence and Security CIS 2014

作者： Li, Wen Li, Hecheng Qinghai Univ Nationalities Dept Math & Stat Xining Peoples R China Qinghai Normal Univ Dept Math Xining 810008 Peoples R China

ISBN: (纸本)9781479974344

In order to solve the constrained multi-objective optimization problems effectively and find a set of Pareto solutions with uniform distribution as well as wide range, in this paper an evolutionary algorithm is proposed based on a space-gridding search technique. Firstly, the decision space is divided into grids and a feasible ratio is defined for each grid. The mutation operations are executed according to this ratio, which can generate as more feasible individuals as possible. In addition, the objective space is also divided into grids to find non-dominated solutions, which can reduce the computation time evidently. Experimental results show that these technologies based on space-gridding can improve the efficiency of the algorithm.

关键词： multi-objective optimization problem evolutionary algorithm space-gridding feasible ratio

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An Improved Differential Evolution for Constrained multi-objective optimization problems 16

An Improved Differential Evolution for Constrained Multi-obj...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Song, Erping Li, Hecheng Wanma, Cuo Qinghai Normal Univ Sch Comp Sci & Technol Xining Peoples R China Qinghai Normal Univ Acad Plateau Sci & Sustainabil Sch Math & Stat Xining Peoples R China Qinghai Normal Univ Sch Math & Stat Xining Peoples R China

ISBN: (纸本)9781665404457

The constrained multi-objective optimization problems (CMOPs) is widely used in real-world applications and always hard to handle especially when the objective number becomes more or the constraints are too stringent. In this manuscript, an improved differential evolution method (IDEM) is proposed based on CMOEA/D as well as newly designed mutation operators. Firstly, one mutation operator is presented to improve infeasible points, in which any infeasible point is taken to divide other points into three groups by using the constraint violation information, and based on the division, a potential better point can be found and utilized to improve other infeasible points by the mutation operation. Then the other mutation operator is provided by designing an objective sorting scheme as well as an individual selection method. These two mutation operators are alternately and self-adaptively adopted in evolution process. Finally, the proposed algorithm is executed on some recent benchmark functions and compared with four state-of-the-art EMO algorithms. The experimental results show that IDEM can efficiently solve the CMOPs.

关键词： multi-objective optimization problem differential evolution constraint-handling method Pareto-optimal solutions

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Hybrid multi-objective Genetic Algorithm for multi-objective optimization problems 27

Hybrid Multi-Objective Genetic Algorithm for Multi-Objective...

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27th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Song Wang, Hongfeng Yang, Di Huang, Min Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781479970162

As a result of important practical significance in real-world engineering applications, multi-objective optimization problem has been one of scientific problems concerned by many researchers. In recent years, genetic algorithm (GA) has begun to be widely used to solve a variety of multi-objective optimization problems due to its population-based search mechanism. In this paper, NSGA-II, which is a most classical multi-objective GA, is investigated and discussed in detail. In order to address the problem of exploitation lacking in the search process of NSGA-II, a local search strategy, which is able to applied in multi-objective optimization domain, is proposed and led into NSGA-II efficiently. Based on a set of benchmark test functions, the experimental results show that the proposed algorithm has demonstrated superior to NSGA-II in terms of convergence and distribution.

关键词： multi-objective optimization problem Genetic algorithm Hybrid genetic algorithm Local search

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Numerical algorithms for generating an almost even approximation of the Pareto front in nonlinear multi-objective optimization problems

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Dolatnezhadsomarin, Azam Khorram, Esmaile Yousefikhoshbakht, Majid Amirkabir Univ Technol Dept Math & Comp Sci 424 Hafez Ave Tehran 15914 Iran Bu Ali Sina Univ Fac Sci Dept Math Hamadan Iran

A multiobjective optimization problem (MOP) returns a set of non-dominated points, the so-called Pareto front. Since this set is usually infinite, it is impossible to generate it completely in practice. Therefore, a discrete approximation of the Pareto front is created. One of the most important features of this approximation is a uniform distribution of points on the full Pareto front in order to present a wide variety of solutions to the decision maker who chooses a final solution. While a few algorithms consider this property, two algorithms based on the Pascoletti-Serafini (PS) scalarization approach are proposed. In addition, six well-known test problems with convex and non-convex Pareto fronts are considered to show the effectiveness of the proposed algorithms. Their results are compared with some algorithms including Normal Constraint (NC), Benson type, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), S-Metric Selection Evolutionary multiobjective Algorithm (SMS-EMOA), Differential Evolution (DE) with Binomial Crossover and MOEA/D-DE. The computational results on CPU time and reasonable distribution of points obtained on the Pareto front show that the presented algorithms perform better than other algorithms on these criteria. In addition, although the proposed algorithms compete closely with some algorithms in terms of CPU time, they have more non-dominated solutions and more appropriate distribution than they do in most problems.

关键词： multi-objective optimization problem Pareto optimal solution Pareto front Pascoletti-Serafini scalarization approach Hyperplane

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Investigating the performance of a surrogate-assisted nutcracker optimization algorithm on multi-objective optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Evangeline, S. Ida Darwin, S. Anandkumar, P. Peter Sreenivasan, V. S. Alagappa Chettiar Govt Coll Engn & Technol Dept Elect & Elect Engn Karaikkudi 630003 Tamilnadu India Dr Sivanthi Aditanar Coll Engn Dept Elect & Commun Engn Tiruchendur 628215 Tamilnadu India VV Coll Engn Dept Mech Engn Tisaiyanvilai 627657 Tamilnadu India Sri Krishna Coll Technol Dept Mech Engn Kovaipudur 641042 Tamilnadu India

This paper introduces a novel surrogate-assisted multi-objective nutcracker optimization algorithm. This algorithm is built upon the recently proposed nutcracker optimization algorithm, drawing inspiration from the behaviours observed in Clark's nutcrackers. The algorithm is developed based on two distinct behaviours exhibited by these birds. To comprehensively evaluate the performance of the proposed algorithm, a dual-pronged approach is adopted. On the one hand, a set of artificial test problems is employed to scrutinize the algorithm's capabilities, while on the other hand, a set of real-world problems is considered to assess its practical efficacy. The results of the proposed algorithm are evaluated in comparison to existing baseline algorithms and state-of-the-art algorithms, using well-recognized performance metrics, both qualitatively and quantitatively. The obtained results provide convincing evidence of the performance of the proposed algorithm.

关键词： multi-objective optimization problem Surrogate-assisted nutcracker optimization algorithm Radial basis function model Performance metrics

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Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Sahu, B. R. B. Bhurjee, A. K. Kumar, P. SRM Inst Sci & Technol Coll Engn & Technol Dept Math Kattankulathur 603203 Tamil Nadu India VIT Bhopal Univ Bhopal Indore Highway Sehore 466114 Madhya Pradesh India Natl Inst Technol Hamirpur DoMSC Hamirpur 177005 HP India

In this paper, a generalized interval vector space is investigated and defined as an ordered relation in the form of a bijective linear transformation of its onto a real vector space. The ordered relation is utilized to formulate an interval optimization problem in the same manner as a classical multi-objective programming problem. A methodology that addresses the existence of efficient solutions for the multi-objective interval optimization problem has also been discussed. Various numerical examples are described to illustrate and substantiate all developed concepts. Furthermore, the multi-objective portfolio rebalancing problem for a time horizon is designed based on the developed interval vector optimization. An algorithm using exhaustive solution technology has been proposed to achieve an efficient investment strategy. Finally, its applicability and efficacy are analyzed using Bombay Stock Exchange India data sets.

关键词： multi-objective optimization problem Interval vector space Interval analysis theory Interval optimization problem Efficient solution Portfolio selection

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Simulated fire observables as indicators for optimizing wireless sensor networks in wildfire risk monitoring

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Ecological Indicators 2025年 175卷

作者： Juan Luis Gómez-González Effie Marcoulaki Alexis Cantizano Myrto Konstantinidou Raquel Caro Mario Castro Institute for Research in Technology (IIT) ICAI School of Engineering Comillas Pontifical University c/ Rey Francisco 4 28008 Madrid Spain System Reliability and Industrial Safety Laboratory Institute of Nuclear & Radiological Sciences & Technology Energy & Safety National Centre for Scientific Research “Demokritos” 15310 Ag. Paraskevi Attiki Athens 60037 Greece University Institute of Studies on Migration (IUEM) Chair in Disasters Fundación AON España c/ Rey Francisco 4 28008 Madrid Spain Grupo Interdisciplinar de Sistemas Complejos (GISC) Comillas Pontifical University c/ Alberto Aguilera 25 28008 Madrid Spain

Uncontrolled wildfires cause significant damage and economic costs. Wireless Sensor Networks (WSNs) can mitigate these impacts by detecting fires early across extensive wildland areas. This work presents a simulation-driven optimization framework for localizing WSNs to enhance early wildfire detection and minimize potential damage. Formulated as a multi-objective optimization problem (MOOP) and solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the method utilizes dynamic wildfire simulations and considers stochastic variables such as ignition likelihood and weather conditions. The methodology is general and independent of the simulation model or the studied region. The framework supports decision-making under uncertainty, ensuring the designed networks remain effective across varying conditions. A practical case study with validated fire behaviour demonstrates the robustness of the approach to identify the most efficient and cost-effective sensor locations. Results show significantly better performance compared to uniform sensor grids and WSNs designed for fixed-weather scenarios, highlighting the benefits of this approach for wildfire management.

关键词： AT Arrival Time BA Burnt Area CA Cellular Automata DBA Detected Burnt Area ECMWF European Centre for Medium-Range Weather Forecasts EFFIS European Forest Fire Information System ETRS89 European Terrestrial Reference System 1989 ERA5 Fifth Generation ECMWF Reanalysis for the Global Climate and Weather EWDS Early Wildfire Detection System FSF Free-Spreading Fire FWI Fire Weather Index GA Genetic Algorithms IoT Internet of Things LBPM Landscape Burnt Probability Map MOOP multi-objective optimization problem NSGA-II Non-Dominated Sorting Genetic Algorithm II SIGIF Integrated Forest Fire Management System of Generalitat Valenciana WII Wildland Industrial Interface WSN Wireless Sensor Network WUI Wildland Urban Interface. Wildfire simulation optimization Early wildfire detection system Wireless sensor network NSGA-II

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Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved multi-objective Particle Swarm optimization

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IEEE ACCESS 2021年 9卷 54264-54275页

作者： Tian, Cenxi Xu, Hongyun Lu, Tao Jiang, Rui Kuang, Yong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved multi-objective Particle Swarm optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

关键词： K-anonymity location privacy location semantic multi-objective optimization problem particle swarm optimization algorithm road networks

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Relay selection and power allocation for energy-efficient cooperative cognitive radio networks

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PHYSICAL COMMUNICATION 2018年 28卷 1-10页

作者： Soleimanpour-Moghadam, M. Talebi, S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In this paper, we apply the innovative multi-objective optimization methods to the challenge posed by rate maximization, total transmission power minimization and relay selection in cooperative cognitive radio networks. The proposed methods which are based on amplify and forward relaying strategy optimize the three conflicting objectives and, at same time, they maximize the rate quality, minimize the total transmission power allocated to the network relays and make the best relay node selection. The multi-objective optimization studied is a non-convex non-linear combinatorial algorithm which is converted to its convex smooth equivalent through two efficient approximation methods. We apply the multi-objective lexicographic method to overcome the challenge posed by these conflicting objectives simultaneously. The proposed relay node selection method is based on zero-norm principle which provides an effective technique to obtain a minimum node selection. Simulation results confirm that the proposed approaches offer superior performance over known schemes in terms of throughput gain and number of active relays. (C) 2017 Published by Elsevier B.V.

关键词： Cooperative cognitive networks Relay selection Zero-norm multi-objective optimization problem Lexicographic method

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