Prospect Theory in Particle Swarm Optimization for Constraints Nonlinear Optimization Problems

2018 ◽  
Author(s):  
Ahmed I. Abdulkareem ◽  
Hayder Abd Dhahad ◽  
Noor Q. Yousif
Keyword(s):  
Particle Swarm Optimization ◽  
Prospect Theory ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Constrained Nonlinear Optimization ◽  
Opposition Based Learning ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance.

Solving Nonlinear Optimization Problems of Steering Trapezoid Mechanism Based on an IPSO

2014 ◽  
Vol 945-949 ◽  
pp. 607-613
Author(s):  
Ling Liu ◽  
Pei Zhou ◽  
Jun Luo ◽  
Zan Pi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Steering Wheel ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Rotational Angle ◽  
Improved Particle Swarm Optimization

The paper focus on an improved particle swarm optimization (IPSO) used to solve nonlinear optimization problems of steering trapezoid mechanism. First of all, nonlinear optimization model of steering trapezoid mechanism is established. Sum of absolute value of difference between actual rotational angle of anterolateral steering wheel and theoretical rotational angle of anterolateral steering wheel is taken as objective function, bottom angle and steering arm length of steering trapezoid mechanism are selected to be design variables. After that, an improved particle swarm optimization algorithm is proposed by introducing Over-flow exception dealing functions to deal with complicated nonlinear constraints. Finally, codes for IPSO are programmed and parameters of steering trapezoid mechanism for different models are optimized, and numerical result shows that errors of objective function's ideal values and objective function's optimization values are minimal. Performance comparison experiment of different intelligent algorithms indicates that the proposed new algorithm is superior to Particle swarm algorithm based on simulated annealing (SA-PSO) and traditional particle swarm optimization (TPSO) in good and fast convergence and small calculating quantity, but a little inferior to particle swarm algorithm based on simulated annealing (SA-PSO) in calculation accuracy in the process of optimization.

scholarly journals Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-peng Wei ◽  
Jian-xia Zhang ◽  
Dong-sheng Zhou ◽  
Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Standard Test ◽  
Engineering Problem ◽  
Particle Swarm Algorithm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Local Optima

We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.

scholarly journals A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jinjin Ding ◽  
Qunjin Wang ◽  
Qian Zhang ◽  
Qiubo Ye ◽  
Yuan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Hammerstein Model ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

scholarly journals Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

2021 ◽  
Author(s):  
Moritz Mühlenthaler ◽  
Alexander Raß ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Markov Model ◽  
Optimization Problems ◽  
Formal Analysis ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Local Optima ◽  
Sorting Problem

AbstractMeta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

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