Tracking Multiple Optima in Dynamic Environments by Quantum-Behavior Particle Swarm Using Speciation

2012 ◽  
Vol 3 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Ji Zhao ◽  
Jun Sun ◽  
Vasile Palade
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Dynamic Environments ◽  
Multimodal Optimization ◽  
Complex Dynamic ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Local Optima ◽  
Dynamic Function ◽  
Quantum Behavior

This paper presents an improved Quantum-behaved Particle Swarm Optimization, namely the Species-Based QPSO (SQPSO), using the notion of species for solving optimization problems with multiple peaks from the complex dynamic environments. In the proposed SQPSO algorithm, the swarm population is divided into species (subpopulations) based on their similarities. Each species is grouped around a dominating particle called species seed. Over successive iterations, species are able to simultaneously optimize towards multiple optima by using the QPSO procedure, so that each of the peaks can be definitely searched in parallel, regardless of whether they are global or local optima. A number of experiments are performed to test the performance of the SQPSO algorithm. The environment used in the experiments is generated by Dynamic Function # 1(DF1). The experimental results show that the SQPSO is more adaptive than the Species-Based Particle Swarm Optimizer (SPSO) in dealing with multimodal optimization in dynamic environments.

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.

scholarly journals An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaobing Yu ◽  
Jie Cao ◽  
Haiyan Shan ◽  
Li Zhu ◽  
Jun Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Stochastic Search ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Local Optima

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems

2011 ◽  
Vol 383-390 ◽  
pp. 7208-7213
Author(s):  
De Kun Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Standard Particle Swarm Optimization ◽  
Fixed Path

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

Multi-objective particle swarm optimization using Pareto-based set and aggregation approach

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740073 ◽  
Author(s):  
Song Huang ◽  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Multi Objective

Multi-objective optimization problems (MOPs) need to be solved in real world recently. In this paper, a multi-objective particle swarm optimization based on Pareto set and aggregation approach was proposed to deal with MOPs. Firstly, velocities and positions were updated similar to PSO. Then, global-best set was defined in particle swarm optimizer to preserve Pareto-based set obtained by the population. Specifically, a hybrid updating strategy based on Pareto set and aggregation approach was introduced to update the global-best set and local search was carried on global-best set. Thirdly, personal-best positions were updated in decomposition way, and global-best position was selected from global-best set. Finally, ZDT instances and DTLZ instances were selected to evaluate the performance of MULPSO and the results show validity of the proposed algorithm for MOPs.

Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2621-2624
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Qun Song Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Single Chip ◽  
Immune Genetic Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Chaos Sequence ◽  
Multimodal Problems

In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

scholarly journals Multi-objective particle swarm optimization with R2 indicator and adaptive method

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Mengke Jiang ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Empirical Studies ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Adaptive Method ◽  
Swarm Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Problem Instances

AbstractMulti-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world.

Optimization of Loneys Solenoid Design Using a Dynamic Search Based Technique

2021 ◽  
Vol 36 (1) ◽  
pp. 35-40
Author(s):  
Shanshan Tu ◽  
Obaid Rehman ◽  
Sadaqat Rehman ◽  
Shafi Khan ◽  
Muhammad Waqas ◽  
...  
Keyword(s):  
Particle Swarm ◽  
Global Optimum ◽  
Test Functions ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Local Optima ◽  
Good Balance ◽  
Dynamic Search ◽  
Optimum Solution ◽  
Global Optimum Solution

Particle swarm optimizer is one of the searched based stochastic technique that has a weakness of being trapped into local optima. Thus, to tradeoff between the local and global searches and to avoid premature convergence in PSO, a new dynamic quantum-based particle swarm optimization (DQPSO) method is proposed in this work. In the proposed method a beta probability distribution technique is used to mutate the particle with the global best position of the swarm. The proposed method can ensure the particles to escape from local optima and will achieve the global optimum solution more easily. Also, to enhance the global searching capability of the proposed method, a dynamic updated formula is proposed that will keep a good balance between the local and global searches. To evaluate the merit and efficiency of the proposed DQPSO method, it has been tested on some well-known mathematical test functions and a standard benchmark problem known as Loney’s solenoid design.

Constrained Optimization by the α Constrained Particle Swarm Optimizer

2005 ◽  
Vol 9 (3) ◽  
pp. 282-289 ◽  
Author(s):  
Tetsuyuki Takahama ◽  
◽  
Setsuko Sakai ◽  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Satisfaction Level ◽  
Particle Swarm Optimizer ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Constrained Problems ◽  
Α Level ◽  
Unconstrained Problems

In this study, α constrained particle swarm optimizer αPSO, which is the combination of the α constrained method and particle swarm optimization, is proposed to solve constrained optimization problems. The α constrained methods can convert algorithms for unconstrained problems to algorithms for constrained problems using the α level comparison, which compares the search points based on the satisfaction level of constraints. In the αPSO, the agents who satisfy the constraints move to optimize the objective function and the agents who don't satisfy the constraints move to satisfy the constraints. The effectiveness of the αPSO is shown by comparing the αPSO with GENOCOP5.0, and other PSO-based methods on some nonlinear constrained problems.

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