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Novel discrete particle swarm optimization Based on Huge Value Penalty for Solving Engineering Problem

Chinese Journal of Mechanical Engineering 2009年第3期22卷 410-418页

作者： YU Ying YU Xiaochun LI Yongsheng Pharmacy College China Pharmaceutical University Nanjing 210009 China College of Mechanical Engineering Nanjing University of Technology Nanjing 210009 China

For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization（DPSO） is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization（PSO）, but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization（DPPSO） and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.

关键词： discrete particle swarm optimization location updating scheme of constraints level huge value penalty optimization design bellows

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A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat

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Defence Technology（防务技术） 2022年第6期18卷 1006-1022页

作者： Wei-hua Li Jing-ping Shi Yun-yan Wu Yue-ping Wang Yong-xi Lyu School of Automation Northwestern Polytechnical UniversityXi'an710129China Shaanxi Province Key Laboratory of Flight Control and Simulation Technology Xi'an710129China Science and Technology on Aircraft Control Laboratory FACRIXi'an710065China

Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air ***,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one *** such,this study presents a practical cooperative occupation decision-making methodology for use with multiple *** weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one *** encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation *** corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage *** resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)*** such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a *** occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two *** results showed these solutions varied with initial conditions and weighting *** occupation process,based on formation switching,effectively demonstrates the viability of the proposed *** cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.

关键词： Unmanned combat aerial vehicle Cooperative occupation Beyond-visual-range air combat Weapon engagement zone discrete particle swarm optimization Formation switching

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维普期刊数据库

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A particle swarm optimization algorithm on the surgery scheduling problem with downstream process 25th

A particle swarm optimization algorithm on the surgery sched...

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第25届中国控制与决策会议

作者： WANG Yu MIAO Yunhui ZHU Huabo TANG Jiafu Dept. of Systems Engineering State Key Lab of Synthetic Automation of Process Industry Northeastern University

ISBN: (纸本)9781467355339

This paper focuses on generating an optimal surgery schedule of elective-patients in multiple operating theatres, which considering the intra-operative care and recovery phases. We try to determine the surgery sequence and location. With regard to the downstream process, recovery room, the problem is modeled as a hybrid integer program with the objective of minimizing the operating costs of hospital, which includes operating rooms’ fixed costs, operating rooms’ overtime costs and recovery costs. A discrete particle swarm optimization algorithm combined with heuristic rules is proposed. We text our approach with realistic data, the results show the algorithm we present basically reached the same level of CPLEX, while the computation time is far less than CPLEX. Additionally, the approach can find the optimized number of daily opening operating rooms and recovery beds through varying parameters in the experiment, which give management insights to hospital and reduce the daily operating cost.

关键词： Operating theatre Scheduling discrete particle swarm optimization Heuristic

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