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A swarm exploring neural dynamics method for solving convex multi-objective optimization problem

NEUROCOMPUTING 2024年 601卷

作者： Zhang, Zhijun Yu, Haomin Ren, Xiaohui Luo, Yamei South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Inst Super Robot Huangpu Guangzhou 510555 Peoples R China Nanchang Univ Jiangxi Thousand Talents Plan Nanchang 330031 Peoples R China Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Guangdong Artifcial intelligence & Digital Econ La Pazhou Lab Guangzhou 510335 Peoples R China Shanxi Univ Technol Sch Mech Engn Shaanx Prov Key Lab Ind Automat Hanzhong 723001 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410100 Peoples R China Guangdong Univ Petrochem Technol Inst Artificial Intelligence & Automat Sch Automat Sci & Engn Maoming 525000 Peoples R China South China Univ Technol Key Lib Autonomous Syst & Network Control Minist Educ Guangzhou 510641 Peoples R China

multi-objective optimization problem (MOP) plays an increasingly important role in finance and engineering. In order to obtain more accurate and evenly distributed target solution set to a multi-objective programming, a novel swarm exploring neural dynamics (SEND) method is proposed, analyzed and applied in this paper. Specifically, a scalarization approach is firstly applied to transform the MOP into a group of subproblems. Secondly, each subproblem is solved by a varying parameter recurrent neural network (VP-RNN). By solving these problems, a group of Pareto optimal solutions are obtained. Thirdly, a population evolution weight optimization algorithm is used to diversify the solution set to obtain evenly distributed solutions. Simulation results demonstrate that the proposed SEND method can obtain a more accurate and evenly distributed solution set than some previous methods and the convergence rate is faster than the state-of-art methods, such as collaborative neurodynamic approach (CNA).

关键词： Swarm exploring neural dynamics multi-objective optimization problem Pareto optimal solutions

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Analysis on multi-objective optimization problem Techniques

Analysis on Multi-objective Optimization Problem Techniques

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Springer International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS)

作者： Jaiswal, Aditi Maulana Azad Natl Inst Technol Comp Sci & Engn Dept Bhopal 462003 Madhya Pradesh India

ISBN: (纸本)9789811319518;9789811319501

In past few years, Web-based application and services are growing rapidly and this growing demands needs different Quality of Services (QoS) requirements for efficient use of such web-based services. The purpose behind utilizing these application resources could be tarnished if the fundamental communication network does not fulfill the QoS requirements. However, different applications have distinct QoS necessities as each application have different priorities. The main concern is to come across such solution which will optimize the network not in the terms of minimum number of hops but in terms of Qos parameters of network, relies upon application running over that network. This issue comes under multi-objective optimization problem (MOOP) and Genetic Algorithm (GA) is one of the techniques which can possibly control numerous parameters all together, and hence GA is applied to solve MOOP, which can enhance the QoS. This paper surveys the various MOOP techniques and then gives the best solution among them.

关键词： multi-objective optimization problem Evolutionary algorithms Quality of service (qos) Genetic algorithm (ga)

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Hybrid QPSO-NNIA2 Algorithm for multi-objective optimization problem

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

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multi-objective optimization problem WITH BOUNDED PARAMETERS

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RAIRO-OPERATIONS RESEARCH 2014年第4期48卷 545-558页

作者： Bhurjee, Ajay Kumar Panda, Geetanjali Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India

In this paper, we propose a nonlinear multi-objective optimization problem whose parameters in the objective functions and constraints vary in between some lower and upper bounds. Existence of the efficient solution of this model is studied and gradient based as well as gradient free optimality conditions are derived. The theoretical developments are illustrated through numerical examples.

关键词： multi-objective optimization problem efficient solution optimality condition interval valued convex function

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The multi-objective optimization problems of Markovian Jump Systems Closed by General Controllers: A Mode-Separation Approach

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13359-13372页

作者： Wang, Guoliang Lyu, Yaqiang Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China

This paper studies the multi-objective optimization problems (MOPs) of Markovian jump systems (MJSs) closed by general controllers. Firstly, the linear quadratic regulator (LQR) problem of MJSs closed by a general controller with sampled or synchronous modes is studied and estimated by a mode-separation approach, whose traditional algebraic Riccati equations (AREs) are removed. Meanwhile, two traditional situations about mode-dependent and -independent controllers are contained as special ones. Secondly, an MOP deeply depending on mode separations is proposed and solved by the non-dominated sorting whale optimization algorithm (NSWOA) such that the minimum value of LQR problem and the maximum expectation value of summed mode dwell times in the same mode separation are simultaneously optimized. Particularly, in order to further improve the estimation effect, a single-objective optimization problem (SOP) is presented and computed by applying the deep deterministic policy gradient (DDPG) technique, whose optimal controller in addition to its best mode separation is given in detail. Thirdly, similarly but more generally, another controller with its mode and state both sampled is constructed to realize the LQR problem, whose effects are better than some existing methods and can also be improved by solving some optimization problems. Finally, a simulation is used to verify the effectiveness and superiority of the proposed methods. Note to Practitioners-This paper addresses the LQR problem for MJSs, commonly used in automation and control with uncertainty and frequent mode switching. Traditional LQR methods rely on mode-dependent controllers and AREs, leading to high computational costs and real-time demands, especially in industrial settings. To overcome these challenges, this paper proposes a general controller design and an MOP, replacing AREs with a mode-separation strategy, simplifying the process and reducing costs. A controller with both mode and state sampling is a

关键词： Control systems optimization Automation Switches Real-time systems Synchronization Sliding mode control Event detection Training Regulators Markovian jump systems multi-objective optimization problem mode-separation approach linear quadratic regulator sampled control

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A Competitive Mechanism multi-objective Particle Swarm optimization Algorithm and Its Application to Signalized Traffic problem

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CYBERNETICS AND SYSTEMS 2020年第1期52卷 73-104页

作者： Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

In this paper, a modified Competitive Mechanism multi-objective Particle Swarm optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.

关键词： Evolutionary algorithms multi-objective optimization problem particle swarm optimization

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

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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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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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A novel approach for the solution of multiobjective optimization problem using hesitant fuzzy aggregation operator

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RAIRO-OPERATIONS RESEARCH 2022年第1期56卷 275-292页

作者： Ahmad, Firoz Adhami, Ahmad Yusuf John, Boby Reza, Amit Indian Stat Inst SQC & OR Unit Bangalore Karnataka India Indian Inst Technol Gandhinagar Gandhinagar Gujarat India Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India

Many decision-making problems can solve successfully by traditional optimization methods with a well-defined configuration. The formulation of such optimization problems depends on crisply objective functions and a specific system of constraints. Nevertheless, in reality, in any decision-making process, it is often observed that due to some doubt or hesitation, it is pretty tricky for decision-maker(s) to specify the precise/crisp value of any parameters and compelled to take opinions from different experts which leads towards a set of conflicting values regarding satisfaction level of decision-maker(s). Therefore the real decision-making problem cannot always be deterministic. Various types of uncertainties in parameters make it fuzzy. This paper presents a practical mathematical framework to reflect the reality involved in any decision-making process. The proposed method has taken advantage of the hesitant fuzzy aggregation operator and presents a particular way to emerge in a decision-making process. For this purpose, we have discussed a couple of different hesitant fuzzy aggregation operators and developed linear and hyperbolic membership functions under hesitant fuzziness, which contains the concept of hesitant degrees for different objectives. Finally, an example based on a multiobjective optimization problem is presented to illustrate the validity and applicability of our proposed models.

关键词： Decision-making problem multi-objective optimization problem hesitant fuzzy membership function hesitant fuzzy aggregation operator

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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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