scholarly journals The Design of Contactors Based on the Niching Multiobjective Particle Swarm Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Wenying Yang ◽  
Jiuwei Guo ◽  
Yang Liu ◽  
Guofu Zhai
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Ideal Point ◽  
Particle Swarm ◽  
Point Theory ◽  
Compromise Solution ◽  
Entropy Weight ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization

Contactors are important components in circuits. To solve the multiobjective optimization problems (MOPs) of contactors, a niching multiobjective particle swarm optimization (NMOPSO) with the entropy weight ideal point theory is proposed in this paper. The new algorithm selecting and archiving the nondominated solutions based on the niching theory to ensure the diversity of the nondominated solutions. To avoid missing the extreme solutions of each objective during the multiobjective optimization process, extra particle swarms used to search the independent optimal solution of each objective are supplemented in this algorithm. In order to determine the best compromise solution, a method to select the compromise solution based on entropy weight ideal point theory is also proposed in this paper. Using the algorithm to optimize the characteristics of a typical direct-acting contactor, the results show that the proposed algorithm can obtain the best compromise solution in MOPs.

scholarly journals Research on Multiaircraft Cooperative Suppression Interference Array Based on an Improved Multiobjective Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Huan Zhang ◽  
Rennong Yang ◽  
Changyue Sun ◽  
Haiyan Han
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Minimum Width ◽  
Air Defense ◽  
Radar Network ◽  
Multiobjective Particle Swarm Optimization

For the problem of multiaircraft cooperative suppression interference array (MACSIA) against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.

scholarly journals Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 827 ◽  
Author(s):  
E. J. Solteiro Pires ◽  
J. A. Tenreiro Machado ◽  
P. B. de Moura Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization ◽  
Multiobjective Algorithms ◽  
Average Fitness

Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON DIMENSIONAL UPDATE

2013 ◽  
Vol 22 (03) ◽  
pp. 1350015 ◽  
Author(s):  
HEMING XU ◽  
YINGLIN WANG ◽  
XIN XU
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Particle Swarm ◽  
Experimental Results ◽  
Entire Region ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Limited Size ◽  
Multiobjective Particle Swarm Optimization ◽  
The Impact

For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i. e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i. e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.

scholarly journals Dynamic Multiaircraft Cooperative Suppression Interference Array Optimization by Dynamic MOPSO Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Huan Zhang ◽  
Rennong Yang ◽  
Changyue Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Air Defense ◽  
Multiobjective Particle Swarm Optimization ◽  
Dynamic Multiobjective Optimization

Dynamic multiaircraft cooperative suppression interference array (MACSIA) optimization problem is a typical dynamic multiobjective optimization problem. In this paper, the sum of the distance between each jamming aircraft and the enemy air defense radar network center and the minimum width of the safety area for route planning are taken as the objective functions. The dynamic changes in the battlefield environment are reduced to two cases. One is that the location of the enemy air defense radar is mobile, but the number remains the same. The other is that the number of the enemy air defense radars is variable, but the original location remains unchanged. Thus, two dynamic multiobjective optimization models of dynamic MACSIA are constructed. The dynamic multiobjective particle swarm optimization algorithm is used to solve the two models, respectively. The optimal dynamic MACSIA schemes which satisfy the limitation of the given suppression interference effect and ensure the safety of the jamming aircraft themselves are obtained by simulation experiments. And then verify the correctness of the constructed dynamic multiobjective optimization model, as well as the feasibility and effectiveness of the dynamic multiobjective particle swarm optimization algorithm in solving dynamic MACSIA problem.

scholarly journals Solving the Dynamic Weapon Target Assignment Problem by an Improved Multiobjective Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (19) ◽  
pp. 9254
Author(s):  
Lingren Kong ◽  
Jianzhong Wang ◽  
Peng Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Conflicting Objectives ◽  
Target Assignment

Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems.

scholarly journals An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Fei Han ◽  
Yu-Wen-Tian Sun ◽  
Qing-Hua Ling
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Pattern ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Multiobjective Particle Swarm Optimization ◽  
Convergence Performance

Although multiobjective particle swarm optimization (MOPSO) has good performance in solving multiobjective optimization problems, how to obtain more accurate solutions as well as improve the distribution of the solutions set is still a challenge. In this paper, to improve the convergence performance of MOPSO, an improved multiobjective quantum-behaved particle swarm optimization based on double search strategy and circular transposon mechanism (MOQPSO-DSCT) is proposed. On one hand, to solve the problem of the dramatic diversity reduction of the solutions set in later iterations due to the single search pattern used in quantum-behaved particle swarm optimization (QPSO), the double search strategy is proposed in MOQPSO-DSCT. The particles mainly learn from their personal best position in earlier iterations and then the particles mainly learn from the global best position in later iterations to balance the exploration and exploitation ability of the swarm. Moreover, to alleviate the problem of the swarm converging to local minima during the local search, an improved attractor construction mechanism based on opposition-based learning is introduced to further search a better position locally as a new attractor for each particle. On the other hand, to improve the accuracy of the solutions set, the circular transposon mechanism is introduced into the external archive to improve the communication ability of the particles, which could guide the population toward the true Pareto front (PF). The proposed algorithm could generate a set of more accurate and well-distributed solutions compared to the traditional MOPSO. Finally, the experiments on a set of benchmark test functions have verified that the proposed algorithm has better convergence performance than some state-of-the-art multiobjective optimization algorithms.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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