Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

scholarly journals WEIGHT MINIMIZATION OF TRUSS STRUCTURES WITH SIZING AND LAYOUT VARIABLES USING INTEGRATED PARTICLE SWARM OPTIMIZER

2017 ◽  
Vol 23 (8) ◽  
pp. 985-1001 ◽  
Author(s):  
Ali MORTAZAVI ◽  
Vedat TOĞAN ◽  
Ayhan NUHOĞLU
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Truss Structures ◽  
Continuous Variables ◽  
Particle Swarm Optimizer ◽  
Search Spaces ◽  
Optimization Constraints ◽  
Weight Minimization ◽  
Ipso Algorithm

This study investigates the performances of the integrated particle swarm optimizer (iPSO) algorithm in the layout and sizing optimization of truss structures. The iPSO enhances the standard PSO algorithm employing both the concept of weighted particle and the improved fly-back method to handle optimization constraints. The performance of the recent algorithm is tested on a series of well-known truss structures weight minimization problems including mixed design search spaces (i.e. with both discrete and continuous variables) over various types of constraints (i.e. nodal dis­placements, element stresses and buckling criterion). The results demonstrate the validity of the proposed approach in dealing with combined layout and size optimization problems.

scholarly journals Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Hai-Feng Ling ◽  
Qiu Guan
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Evolutionary Information ◽  
Adaptive Mechanism ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Adaptive Parameters ◽  
Individual Particles

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

Parallelizing a Modified Particle Swarm Optimizer (PSO)

2010 ◽  
Vol 163-167 ◽  
pp. 2404-2409 ◽  
Author(s):  
Bin Yang ◽  
Qi Lin Zhang
Keyword(s):  
Execution Time ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Topological Optimization ◽  
Particle Swarm Optimizer ◽  
Speed Up ◽  
Truss Topological Optimization ◽  
High Computational Cost

Recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. Since this optimizer belongs to evolutionary algorithm and plagued by high computational cost as measured by execution time, in order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. The performance of the proposed algorithm is tested by two examples and promising speed-up rate is obtained. Final part is conclusion and outlook.

scholarly journals Design of Two-Dimensional Recursive Digital Filter Using Multi Particle Swarm Optimization Algorithm

2020 ◽  
Vol 53 (4) ◽  
pp. 559-566
Author(s):  
Lakhdar Kaddouri ◽  
Amel B.H. Adamou-Mitiche ◽  
Lahcene Mitiche
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Fast Convergence ◽  
Infinite Impulse Response ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Local Optima

Particle Swarm Optimization (PSO) is an evolutionary algorithm widely used in optimization problems. It is characterized by a fast convergence, which can lead the algorithm to stagnate in local optima. In the present paper, a new Multi-PSO algorithm for the design of two-dimensional infinite impulse response (IIR) filters is built. It is based on the standard PSO and uses a new initialization strategy. This strategy is relayed to two types of swarms: a principal and auxiliaries. To improve the performance of the algorithm, the search space is divided into several areas, which allows a best covering and leading to a better exploration in each zone separately. This solved the problem of fast convergence in standard PSO. The results obtained demonstrate the effectiveness of the Multi-PSO algorithm in the filter coefficients optimization.

scholarly journals MODIFIED PSO ALGORITHM WITH NON-LINEARLY DECREASING INERTIA WEIGHT

2021 ◽  
pp. 21586-215815
Author(s):  
Rayaan Grewal
Keyword(s):  
Velocity Vector ◽  
Early Stage ◽  
Full Range ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Fine Tuning ◽  
Position Vector ◽  
Inertia Weight

This paper proposes a modified particle swarm optimization method with non linearly decreasing inertia weight (MPSO-NDIW) and time varying acceleration coefficients. In this MPSO-NDIW method, proper control of the global exploration and local exploitation is done in finding the optimum solution efficiently. In the early stage, full range of search space is allowed for search by the PSO algorithm and in the later stages of search, fine tuning of solution is done so that the algorithm converges to the global optima efficiently .In velocity vector equation inertia weight and acceleration coefficients are non-linearly varied with iterations. To compare the performance of the MPSO-NDIW method with the other improved PSO, four well known benchmark test functions are used. The results reveal that MPSO-NDIW is a efficient technique and has better performance. KeyWords - Acceleration coefficients, particle swarm, asymmetric initialization, velocity vector, position vector.

scholarly journals Constrained Mixed-Variable Design Optimization Based on Particle Swarm Optimizer with a Diversity Classifier for Cyclically Neighboring Subpopulations

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2016
Author(s):  
Tae-Hyoung Kim ◽  
Minhaeng Cho ◽  
Sangwoo Shin
Keyword(s):  
Phase Velocity ◽  
Information Exchange ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Reliability And Validity ◽  
Search Space ◽  
Mixed Integer ◽  
Particle Swarm Optimizer ◽  
Engineering Design Problems ◽  
Three Phase

In this research, an easy-to-use particle swarm optimizer (PSO) for solving constrained engineering design problems involving mixed-integer-discrete-continuous (MIDC) variables that adopt two kinds of diversity-enhancing mechanisms to achieve superior reliability and validity was developed. As an initial diversity-boosting tool, the local neighborhood topology of each particle is set up such that information exchange is restricted to a limited number of consecutively numbered particles. This topological mechanism forces each particle to move in the search space while interacting only with its neighboring subpopulation. The second diversity-enhancing task is to ensure that the exploration behavior of each particle in the search space is governed such that it follows the diversity classifier decision applied to its subpopulation. This diversity classification iteratively adjusts the three-phase velocity-related mechanism of each particle such that it approaches or retreats from its previous best position/the current best position among the subpopulation. In summary, this PSO tool not only introduces the social interaction of the particle within its cyclically neighboring subpopulation but also exploits the three-phase velocity behavior law governed by the distributed diversity measures categorized for each neighboring subpopulation. This scheme has superior reliability, as well as high practicality for engineering optimization problems involving MIDC variables, which are handled by the widely adopted straightforward rounding-off technique used in most swarm-inspired metaheuristic search technologies.

Sign in / Sign up

Export Citation Format

Share Document