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A novel multi-objective bi-level programming problem under intuitionistic fuzzy environment and its application in production planning problem

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COMPLEX & INTELLIGENT SYSTEMS 2022年第4期8卷 3263-3278页

作者： Singh, V. P. Sharma, Kirti Chakraborty, Debjani Ebrahimnejad, Ali Visvesvaraya Natl Inst Technol Nagpur Dept Math Nagpur 440010 Maharashtra India Indian Inst Technol Kharagpur Dept Math Kharagpur 721302 W Bengal India Islamic Azad Univ Dept Math Qaemshahr Branch Qaemshahr Iran

This paper presents an optimization method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The idea is based on TOPSIS (technique for order preference by similarity to ideal solution) method. TOPSIS method is a multiple criteria method that identifies a satisfactory solution from a given set of alternatives based on the minimization of distance from an ideal point and maximization of distance from the nadir point simultaneously. A new model of multi-objective bi-level programming problem in an intuitionistic fuzzy environment has been considered. The problem is first reduced to a conventional multi-objective bi-level linear programming problem using accuracy function. Then the modified TOPSIS method is proposed to solve the problem at both the leader and the follower level where various linear/non-linear membership functions are used to represent the flexibility in the approach of decision-makers (DMs). The problem is solved hierarchically, i.e., first the problem at the leader level is solved and then the feasible region is extended by relaxing the decision variables controlled by the leader. The feasible region is extended to obtain a satisfactory solution for the DMs at both levels. Finally, the application of the proposed approach in the production planning of a company has been presented. An illustrative numerical example is also given to explain the methodology defined in this paper.

关键词： multi-objective optimization problem Bi-level programming problem TOPSIS method Intuitionistic fuzzy number Fuzzy optimization

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Novel electromagnetism-like mechanism method for multiobjective optimization problems

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Journal of Systems Engineering and Electronics 2015年第1期26卷 182-189页

作者： Lixia Han Shujuan Jiang Shaojiang Lan School of Computer Science and Technology China University of Mining and Technology

As a new-style stochastic algorithm, the electromagnetism-like mechanism（EM） method gains more and more attention from many researchers in recent years. A novel model based on EM（NMEM） for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.

关键词： electromagnetism-like mechanism(EM) method multi-objective optimization problem particle Pareto optimal solutions

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multi-objective optimal design of semi-active fluid viscous dampers for nonlinear structures using NSGA-II

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STRUCTURES 2020年 24卷 678-689页

作者： Bakhshinezhad, Sina Mohebbi, Mohtasham Univ Mohaghegh Ardabili Fac Engn Ardebil *** Iran

This paper presents an effective method to design semi-active fluid viscous damper (SAFVD) system for mitigating the seismic responses of the nonlinear frames considering multiple safety and convenience criteria. This method is based on defining a multi-objective optimization problem with discrete objective functions of minimization of inter-story drift as safety criterion as well as absolute acceleration as convenience criterion where an improved version of the non-dominated sorting genetic algorithm (NSGA-II) has been used to solve it and find out the Pareto-optimal solutions. For this purpose, the maximum damping coefficient of the semi-active damper and parameters of the control algorithm have been selected as design variables. The effect of the uncertainty of the input seismic excitation has been considered using the mean responses under multiple real earthquakes recommended for the site that the structure is located in. To assess the effectiveness of the proposed methodology, a numerical example has been conducted on an eight-story nonlinear shear building frame with hysteretic bilinear elasto-plastic behavior. The results show that the designed optimal SAFVD system has the capability of a significant reduction in seismic responses and improving both the safety and convenience of the entire building. Also comparing the seismic performance of SAFVD system with that of active control system shows that SAFVD could perform as effective as active control system in structural response mitigation.

关键词： Semi-active fluid viscous damper multi-objective optimization problem Non-dominated sorting genetic algorithm (NSGA-II) Nonlinear structure Instantaneous optimal control multiple design earthquakes

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A computational intelligence approach to system-of-systems architecting incorporating multi-objective optimization 1

A computational intelligence approach to system-of-systems a...

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Conference on Systems Engineering Research

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： System-of-Systems SoS Architecting Genetic Algorithm GA multi-objective optimization problem MOP multi-objective Evolutionary Algorithm MOEA Meta-Architecture

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A new multi-objective evolutionary algorithm for solving high complex multi-objective problems 06

A new multi-objective evolutionary algorithm for solving hig...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Li, Kangshun Yue, Xuezhi Kang, Lishan Chen, Zhangxin Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Jiangxi Normal Univ Prov Key Lab High Performance Comp Technol Nanchang 330022 Jiangxi Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Hubei Peoples R China Southern Methodist Univ Ctr Sci Computat Dallas TX 75275 USA Southern Methodist Univ Dept Math Dallas TX 75275 USA

ISBN: (纸本)9781595931863

In this paper, a new multi-objective evolutionary algorithm for solving high complex multi-objective problems is presented based on the rule of energy minimizing and the law of entropy increasing of particle systems in phase space, Through the experiments it proves that this algorithm can quickly obtains the Pareto solutions with high precision and uniform distribution. And the results of the experiments show that this algorithm can avoid the premature phenomenon of problems better than the traditional evolutionary algorithm because it can drive all the individuals to participate in the evolving operation in each generation.

关键词： evolutionary algorithm multi-objective optimization problem free energy entropy Pareto front

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multi-objective interval fractional programming problems : An approach for obtaining efficient solutions

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OPSEARCH 2015年第1期52卷 156-167页

作者： Bhurjee, Ajay Panda, Geetanjali Indian Inst Technol Kharagpur Kharagpur 721302 WB India Indian Inst Technol Kharagpur Fac Math Dept Math Kharagpur 721302 WB India

This paper addresses a general multi-objective fractional programming problem whose parameters in the objective functions and constraints are intervals. Existence of the efficient solution of this model is studied. A methodology is developed to determine its efficient solutions. This methodology is illustrated through a numerical example.

关键词： Interval optimization multi-objective optimization problem Efficient solution Fractional programming problem

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Optimal Placement of Data Concentrators and Repeaters in PLC-enabled Smart Grids

Optimal Placement of Data Concentrators and Repeaters in PLC...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Stiri, Souhaima Chaoub, Abdelaali Grilo, Antonio Bennani, Rachid Lakssir, Brahim Tamtaoui, Ahmed INPT MUSICS Team STRS Lab Rabat Morocco MAScIR D&MSD Lab Rabat Morocco Univ Lisbon IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781665435970

The focus of this paper is on Power Line Communication (PLC) technology which has a great potential to accommodate the huge amount of data produced by different applications and nodes in the context of Smart Grids (SGs). In particular, this paper dives deeper into the planning and placement of Data Concentrator (DC) in various territorial categories: urban, suburban and rural environments. Supplemental repeaters, usually omitted in previous stat-of-the-art, are inserted throughout the electrical grid to reach the entire network. A multi-criteria optimization approach has been followed and the popular NSGA-II algorithm has been exploited to find a good trade-off between the conflicting performance and profitability objectives. Results of extensive numerical simulation are provided confirming the effective need of repeaters especially in remote areas where Smart Meters (SMs) are sparsely distributed. Moreover and despite the destructive influence of fading and noise along with the involved investments, PLC is still able to provide a satisfactory balance between performance and cost.

关键词： Smart Metering PLC Data Concentrator and Repeater Placement multi-objective optimization problem multi-objective Evolutionary Algorithm

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An Improved multi-objective optimization Algorithm Based on Reinforcement Learning 13th

An Improved Multi-objective Optimization Algorithm Based on ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Jun Zhou, Yi Qiu, Yimin Li, Zhongfeng Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Yingkou Inst Technol Sch Elect Engn Yingkou 115014 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

multi-objective optimization (MOP) has been widely applied in various applications such as engineering and economics. MOP is an important practical optimization problem, and finding approaches to better solve it is of both practical and theoretical significance. The core of solving an MOP is to find the global optimal solution set efficiently and accurately. The current MOP algorithm has premature convergence or poor population diversity, and the solution set obtained falls easily into the local optimal or clustering phenomenon. In this study, an MOP algorithm based on the non-dominated sorting genetic algorithm based on reinforcement learning (RL-NSGA-II) is proposed;the algorithm is adopted based on the prediction, forecasting, Monte Carlo method, which is based on the action of population genetic information, and environment interaction information and Markov decision process in mathematical modeling. This is because using a non-dominated solution contains valuable information, which can be used to guide the evolution direction of the population and search the optimal solution set more accurately. The proposed RL-NSGA-II algorithm was evaluated on the ZDT and DTLZ test sets, and the experimental results verified the effectiveness of the proposed algorithm in solving MOPs.

关键词： Reinforcement learning NSGA-II Monte Carlo method multi-objective optimization problem

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Nonlinear Equation Systems Solved by many-objective Hype

Nonlinear Equation Systems Solved by many-objective Hype

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IEEE Congress on Evolutionary Computation (CEC)

作者： Qin, Sha Zeng, Sanyou Dong, Wei Li, Xi China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Shijiazhuang Univ Econ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China

ISBN: (纸本)9781479974924

A difficulty in solving nonlinear equation systems (NESs) stays in finding all the solutions for NES. This paper uses multi-objective evolutionary techniques to overcome it. We converted the NES into a multi-objective optimization problem (MOP) with a parameter C. The Pareto-optimal set of the MOP becomes the solutions of the NES when the parameter C gets to infinity. Next, a multi-objective evolutionary algorithm (MOEA) is used to solve the transformed MOP, during which C is gradually approaching infinity. A significant feature of this algorithm is that there is one-to-one relationship between the Pareto optimal set and the Pareto front, which suggests that different solutions have different objective values in the MOP. Thus the MOEA can find multi-solutions of the NES in a single run. Since the MOP is a multi-objective problem in many cases, this paper applies an advanced multi-objective evolutionary algorithm (i.e., Hype algorithm) to solve NES. Our experiment shows better results than or competitive to the four mentioned single-objective optimization in a set of test cases.

关键词： multi-objective optimization problem transformational MOP Nonlinear equation systems Hype algorithm

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IMPROVED PARETO OPTIMUM PASSING USING VARIED KICKING SPEED IN SOCCER GAMES 3

IMPROVED PARETO OPTIMUM PASSING USING VARIED KICKING SPEED I...

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3rd Asian Conference on Intelligent Games and Simulation / 3rd Asian Simulation Technology Conference

作者： Tanjapatkul, Nattawit Kotrajaras, Vishnu Chulalongkorn Univ Fac Engn Dept Comp Engn Payathai Rd Bangkok 10330 Thailand

ISBN: (纸本)9789077381601

Ball passing is very important in soccer games. A good pass to a teammate can create a scoring chance, which is critical for winning the game. This paper presents the improvement of ball passing for Artificial Intelligence (AI) in soccer games that uses pareto optimum passing. We have developed our own soccer simulation using Unity3D game engine. An approachfor finding suitable values for risk parameters on each position on the pitch is proposed. We also introduce the algorithm for calculating a suitable kicking force in order to reduce risk value of positions on the pitch, allowing previously too risky positions to be included in the passing decision algorithm. The results show that AI using the proposed algorithm has better scoring chances and wins more matches than the same AI that does not recalculate the kicking force. This proves our methodology can increase the performance of ball passing for AI in soccer games.

关键词： Soccer Games Pareto Optimality multi-objective optimization problem Artificial Intelligence

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