Request PDF On May 1, 2014, Shih-Chang Wang and others published A modified particle swarm optimization for aggregate production planning Find, read and cite all the research you need on
2012-6-9 · Particle swarm optimization (PSO) has been widely used in multi-objective engineering design optimization where parameter selection is of prime importance. This paper proposes a multi-objective particle swarm optimizer (MOPSO) with a modified crowding factor and enhanced local search ability.
This paper develops a particle swarm optimization (PSO) based framework for constrained optimization problems (COPs). Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011) algorithm.
A modified particle swarm optimizer Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic
To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with
2017-7-19 · Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling Pedro Faria, João Soares, Zita Vale, Hugo Morais and Tiago Sousa Abstract—The elastic behavior of the demand consumption jointly used with other available resources such as distributed
2017-3-3 · optimization of aggregate production planning problem. TVACPSO is a modified and updated form of Particle Swarm Optimization (PSO). Most practical decisions made to solve APP problems usually consider total costs; we have eliminated other objective functions of APP in this case. There was several variables problem with constraints
2020-2-19 · In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish
Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock market data is a challenging task. A large number of learning algorithms are developed to study market behaviours and enhance the prediction accuracy; they have been optimized using swarm and evolutionary computation such as particle
This research extends the hybrid particle swarm optimization-based metaheuristic to solve the fuzzy parallel machine scheduling problems with bell-shaped fuzzy processing times. In this paper, we propose a discrete particle swarm optimization (DPSO) which comprises two components: a particle swarm optimization and genetic algorithm.
It determines aggregate capacity level in factories for a given amount of periods, while without determining the quantity of each individual stock-keeping unit will be produced. The level of details makes APP a useful tool for thinking about decisions with an intermediate time frame that is too early to determine production levels by stock
A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO The new technique converts all objective functions to a single objective function by deriving a single aggregate objective function using specified or selected weighting factors. Since the optimal PID controller parameters are dependent on the
2020-6-4 · Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very . Particle swarm optimization for the estimation of surface complexation constants with the geochemical model PHREEQC-3.1. 2 free download
2020-2-19 · In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae
It determines aggregate capacity level in factories for a given amount of periods, while without determining the quantity of each individual stock-keeping unit will be produced. The level of details makes APP a useful tool for thinking about decisions with an intermediate time frame that is too early to determine production levels by stock
4.1. Particle Swarm Optimization. PSO was proposed by Kennedy and Eberhart and Lian pointed out that this method was a stochastic optimization method based on swarm intelligence and PSO was inspired by the social behavior of bird flocking and their means of information exchange. Due to its easy implementation and fast convergence, PSO has been
Since the optimal PID controller parameters are dependent on the selected weighting factors, the weighting factors was also treated as dynamic optimizing parameters within the particle swarm optimization as a dual optimization and global selection of PID controller optimal parameters as well as best set of weighting factors.
A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Conversion and Management. v53 i1. 75-83. Google Scholar [42]. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection.
2014-10-16 · Recently Huang [7] has proposed Particle Swarm optimization based new routing protocol to reduce packet loss rate in theoretical VANET scenario. In this paper, we have tried to find out optimal parameters configuration of DSDV protocol using Particle Swarm Optimization (PSO). PSO is a population based stochastic
2020-6-4 · Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very . Particle swarm optimization for the estimation of surface complexation constants with the geochemical model PHREEQC-3.1. 2 free download
Particle swarm with extended memory for multiobjective optimization[C]//IEEE Swarm Intelligence Symposium. Indianapolis, USA, 2003: 193-197. [10]郑友莲，樊俊青.多目标粒子群优化算法研究[J].湖北大学学报:自然科学版, 2008, 30(4): 351-355.
Proportional integral derivative controller tuning via Kronecker summation and modified particle swarm optimization with experimental validation Ghooi et al. Published online: 19 Feb 2020 AN ENTROPY-BASED AGGREGATE METHOD FOR MINIMAX OPTIMIZATION. LI XINGSI . Pages: 277-285.
Particle swarm optimization (PSO): is a population-based swarm intelligence algorithm. It was first introduced by Kennedy and Eberhart [21] as a simulation of the social behavior of social organisms, such as bird flocking and fish schooling. PSO uses the physical movement of the individuals (particles) in the
2014-10-16 · Recently Huang [7] has proposed Particle Swarm optimization based new routing protocol to reduce packet loss rate in theoretical VANET scenario. In this paper, we have tried to find out optimal parameters configuration of DSDV protocol using Particle Swarm Optimization (PSO). PSO is a population based stochastic
2015-5-19 · 3. Particle Swarm Optimization . PSO method was developed in 1995 by Kennedy, Eberhart and Shi and it has been implemented successfully in many research fields [14, 15]. PSO is a population-based optimization method in which every solution is regarded as a . Recent Advances in Computer Science ISBN: 978-1-61804-297-2 247
2020-5-28 · particle swarm optimization has been proposed for DOCRs coordination problem. In [4] DOCRs was coordinated for microgrid in both of microgrid operation modes as separate-ly using PSO. A modified particle swarm optimization in [9] has been used DOCRs coordination problem solving for microgrid system only in islanded mode.
2017-2-23 · Particle Swarm Optimization for Aggregators in Smart Grids Demand Response Program Nattachat Wisittipanit Department of Material Engineering School of Science, Mae Fah Luang University, Chiang Rai, Thailand Tel: (+66) 53-916-768, Email: [email protected] Warisa Wisittipanich† Department of Industrial Engineering
Thread / Post : Tags: Title: matlab code economic load dispatch using particle swarm optimization Page Link: matlab code economic load dispatch using particle swarm optimization Posted By: vish_uday Created at: Sunday 16th of April 2017 05:00:02 AM: binary particle swarm optimization matlab code in economic load dispatch, using hopfield neural network for economic dispatch of power system
In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae
2014-6-17 · The aggregate moving vector is a linear combination of every simple behaviour rule vector. The moving vector coefficients should be identified and optimised to have a realistic flocking moving behaviour. We proposed two methods to optimise these coefficients, by using genetic algorithm (GA) and particle swarm optimisation algorithm (PSO).
Proportional integral derivative controller tuning via Kronecker summation and modified particle swarm optimization with experimental validation Ghooi et al. Published online: 19 Feb 2020 AN ENTROPY-BASED AGGREGATE METHOD FOR MINIMAX OPTIMIZATION. LI XINGSI . Pages: 277-285.