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Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

COGNITIVE NEURODYNAMICS 2024年第4期18卷 2095-2110页

作者： Jahangiri, Mohammadreza Nazemi, Alireza Shahrood Univ Technol Fac Math Sci POB ***-316 Shahrood Iran

A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.

关键词： multi-objective optimization problem Neural networks Convex quadratic programming problem Pareto optimal solution Stability Convergence

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Solving multi-objective optimization problems using Differential Evolution Algorithm with Different Population Initialization Techniques 7

Solving Multi-Objective Optimization Problems using Differen...

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7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Devika, K. Jeyakumar, G. Amrita VishwaVidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781538653142

The researchers of Evolutionary Computing (EC) community proposing new and different algorithmic strategies to tackle the increasing issues in handling optimization problems. As the number of objectives in an optimization problem increases the algorithmic complexity in solving the problem also increases. The way the initial population for an optimization problem generated is greatly affecting the performance of the Evolutionary Algorithms (EAs). This paper investigates the performance of Differential Evolution (DE) in solving Mutli-objective optimization problems (MOOP) with two different population initialization (PI) techniques. The performance of different instances of DE is compared based on the solution accuracy obtained. The results obtained shows that DE shows different performance for different PI techniques.

关键词： multi-objective optimization problem Differential Evolution Population Initialization Evolutionary Algorithm

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A Species-Based multi-objective Genetic Algorithm for multi-objective optimization problems 11

A Species-Based Multi-Objective Genetic Algorithm for Multi-...

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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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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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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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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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