Enhanced Particle Swarm Optimization Based on Reference Direction and Inverse Model for Optimization Problems

Reinforcement Learning ◽

Normal Distribution ◽

Random Number ◽

Optimization Problems ◽

Numerical Test ◽

Particle Swarm ◽

Learning Method ◽

Swarm Optimization ◽

Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function by a random number generated from a well-selected normal distribution. The mean and variance of the normal distribution are estimated from the current state of each individual through a policy net. The historic behavior of the swarm group is learned to update the policy net and guide the selection of parameters of the normal distribution. The proposed algorithm is tested with numerical test functions and the results show that the convergence rate of PSO can be improved with the proposed Reinforcement Learning method (RL-PSO).

Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2019070102 ◽

2019 ◽

Vol 10 (3) ◽

pp. 19-38

Author(s):

Farid Bourennani

Keyword(s):

High Performance ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Parallel Particle Swarm Optimization ◽

Classical Computing ◽

Enhanced Particle Swarm Optimization ◽

Asynchronous Parallel

Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

Feasibility enhanced particle swarm optimization for constrained mechanical design problems

10.1177/0954406216681593 ◽

2016 ◽

Vol 232 (2) ◽

pp. 381-400 ◽

Cited By ~ 5

Author(s):

Mehmet Sinan Hasanoglu ◽

Melik Dolen

Keyword(s):

Constraint Satisfaction ◽

Optimization Problems ◽

Particle Swarm ◽

Search Space ◽

Gear Train ◽

Satisfaction Level ◽

Objective Functions ◽

Swarm Optimization ◽

Constrained optimization problems constitute an important fraction of optimization problems in the mechanical engineering domain. It is not uncommon for these problems to be highly-constrained where a specialized approach that aims to improve constraint satisfaction level of the whole population as well as finding the optimum is deemed useful especially when the objective functions are very costly. A new algorithm called Feasibility Enhanced Particle Swarm Optimization (FEPSO), which treats feasible and infeasible particles differently, is introduced. Infeasible particles in FEPSO do not need to evaluate objective functions and fly only based on social attraction depending on a single violated constraint, called the activated constraint, which is selected at each iteration based on constraint priorities and flight occurs only along dimensions of the search space to which the activated constraint is sensitive. To ensure progressive improvement of constraint satisfaction, particles are not allowed to violate a satisfied constraint in FEPSO. The highly-constrained four-stage gear train problem and its two variants introduced in this paper are used to assess the effectiveness of FEPSO. The results suggest that FEPSO is effective and consistent in obtaining feasible points, finding good solutions, and improving the constraint satisfaction level of the swarm as a whole.

Solution of Inverse Convection Heat Transfer Problem Using an Enhanced Particle Swarm Optimization Algorithm

Journal of Heat Transfer ◽

10.1115/1.4004854 ◽

2011 ◽

Vol 134 (1) ◽

Cited By ~ 3

Author(s):

Peng Ding

Keyword(s):

Heat Transfer ◽

Heat Flux ◽

Optimization Problems ◽

Convection Heat Transfer ◽

Particle Swarm ◽

Unknown Boundary ◽

Convection Heat ◽

Swarm Optimization ◽

The main objective of this paper is to solve the inverse convection heat transfer problems with particle swarm optimization method. An enhanced particle swarm optimization (EPSO) algorithm is proposed to overcome the shortcoming of earlier convergence of standard PSO algorithms. The performance of EPSO is tested by some benchmark functions; it is shown that EPSO has a strong antilocal trap capability especially for high dimensional multimodal optimization problems. At last, EPSO is used to identify the unknown boundary heat flux in a channel flow. According to the computational results of four test problems, it is clear that the proposed EPSO algorithm is able to estimate the unknown heat flux accurately even when the input data contain measurement error.

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

Interactive enhanced particle swarm optimization: A multi-objective reliability application

10.1243/1748006xjrr51 ◽

2007 ◽

Vol 221 (3) ◽

pp. 177-191 ◽

Cited By ~ 5

Author(s):

M. K. Pandey ◽

M. K. Tiwari ◽

M. J. Zuo

Keyword(s):

Decision Maker ◽

Optimization Problems ◽

Particle Swarm ◽

Reliability Optimization ◽

Swarm Optimization ◽

Multi Objective ◽

New Variant ◽

Conflicting Objectives ◽

In reliability optimization problems, it is desirable to address different conflicting objectives. This generally includes maximization of system reliability and minimization of cost, weight, and volume. The proposed algorithm of a metaheuristic nature is designed to address multi-objective problems. In the presented algorithm, interaction with a decision maker guides the search towards the preferred solution. A comparison between an existing solution and the newly generated solution substantiates the desirability or fitness of the latter. Further, the utility function expresses the preference information of the decision maker while searching for the best solution. During the development of the algorithm, a new variant of particle swarm optimization (PSO) is proposed and named as ‘enhanced particle swarm optimization’ (EPSO). EPSO considers the difference between the particle's best position and the global best position for efficient search and convergence. The developed algorithm is applied to the reliability optimization problem of a multistage mixed system with four different value functions that are used to simulate the designer's opinion in the solution evaluation process. Results indicate that the algorithm effectively captures the decision maker's preferences for different structures. Superior results in multi-objective reliability problem-solving prove the algorithm's superiority over other approaches.

Fuzzy Particle Swarm Optimization for Constrained Optimization Problems

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2007.00689 ◽

2011 ◽

Vol 30 (5) ◽

pp. 1218-1221 ◽

Cited By ~ 3

Author(s):

Jing-xuan Wei ◽

Yu-ping Wang

Keyword(s):

Constrained Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Constrained Optimization Problems ◽

Swarm Optimization

Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization

Journal of Mechanical Science and Technology ◽

10.1007/s12206-021-0202-3 ◽

2021 ◽

Vol 35 (3) ◽

pp. 875-887

Author(s):

Jin Guo ◽

Zhendong Li ◽

Mengxuan Zhang

Keyword(s):

Parameter Identification ◽

Phenomenological Model ◽

Particle Swarm ◽

Magnetorheological Fluid ◽

Swarm Optimization ◽

Fluid Dampers ◽

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

Natural Computing ◽

10.1007/s11047-021-09856-0 ◽

2021 ◽

Author(s):

Moritz Mühlenthaler ◽

Alexander Raß ◽

Manuel Schmitt ◽

Rolf Wanka

Keyword(s):

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.

Chunking and cooperation in particle swarm optimization for feature selection

Annals of Mathematics and Artificial Intelligence ◽

10.1007/s10472-021-09752-4 ◽

2021 ◽

Author(s):

Malek Sarhani ◽

Stefan Voß

Keyword(s):

Feature Selection ◽

Cooperative Learning ◽

Optimization Problems ◽

Practical Importance ◽

Particle Swarm ◽

Large Datasets ◽

Social Phenomena ◽

Swarm Optimization ◽

Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽

10.3390/en14154613 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4613

Author(s):

Shah Fahad ◽

Shiyou Yang ◽

Rehan Ali Khan ◽

Shafiullah Khan ◽

Shoaib Ahmed Khan

Keyword(s):

Optimization Problems ◽

Quantum Particle ◽

Particle Swarm ◽

Continuous Variables ◽

Design Problems ◽

Swarm Optimization ◽

Electromagnetic Design ◽

Quantum Particle Swarm Optimization ◽

Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.