scholarly journals The Modified Particle Swarm Optimization for a Special Case of the Assignment Problem: A Case Study in Chicken Transportation

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Naratip Supattananon ◽  
Raknoi Akararungruangkul
Keyword(s):  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Optimization Software ◽  
Modified Particle Swarm Optimization ◽  
Special Case ◽  
Modified Pso

This research aims at solving the special case of multistate assignment problem. The problem includes many special characteristics which are not normally included in the assignment problem. There are many types and conditions of vehicles included in the planning and different road conditions of traveling, which would have different effects on fuel consumption, which is the objective function of the study. The proposed problem is determined as a large and complicated problem making the optimization software unable to find an optimal solution within the proper time. Therefore, the researchers had developed a method for determining the optimal solution by using particle swarm optimization (PSO) in which the methods were developed for solving the proposed problem. This method is called the modified particle swarm optimization (modified PSO). The proposed method was tested with three groups of tested instances, i.e., small, medium, and large groups. The computational result shows that, in small-sized and medium-sized problems, the proposed method performed not significantly different from the optimization software, and in the large-sized problems, the modified PSO method gave 3.61% lower cost than the cost generated from best solution generated from optimization software within 72 hours and it gave 11.03% better solution than that of the best existing heuristics published so far (differential evolution algorithm).

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1389-1393
Author(s):  
Ren Tao Zhao ◽  
You Yu Wang ◽  
Hua De Li ◽  
Jun Tie
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Infrared Image ◽  
Optimal Solution ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Modified Pso

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

High Precision Time Synchronization Algorithm for Smart Grid Based on Adaptive Filtering with Modified Particle Swarm Optimization

2013 ◽  
Vol 860-863 ◽  
pp. 2501-2506
Author(s):  
Wen Hua Han ◽  
Xiao Hui Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Grid ◽  
High Precision ◽  
Adaptive Filtering ◽  
Time Synchronization ◽  
Particle Swarm ◽  
Electric Power System ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

The time synchronization network provides time benchmark tasks for various services in electric power system. With the development of the power grid, the applications require more and more accurate time synchronization precision. In this paper, a method of time synchronization based on adaptive filtering with a modified particle swarm optimization (MPSO-AF) was presented to satisfy the high precision and high security requirements of the time synchronization for smart grid. The modified PSO was introduced for tuning the weight coefficients of the adaptive filter to improve the filtering property. The proposed MPSO-AF hybrid algorithm can combine the advantageous properties of the modified PSO and the adaptive filtering algorithm to enhance the performance of the time synchronization. A comparison of simulation results shows the optimization efficacy of the algorithm.

Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine

2011 ◽  
Vol 128-129 ◽  
pp. 113-116 ◽  
Author(s):  
Zhi Biao Shi ◽  
Quan Gang Song ◽  
Ming Zhao Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with traditional particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm support vector machine (GA-SVM).

scholarly journals A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Modified Pso

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

scholarly journals Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

scholarly journals An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3721 ◽  
Author(s):  
Chun-Yao Lee ◽  
Maickel Tuegeh
Keyword(s):  
Particle Swarm Optimization ◽  
Economic Dispatch ◽  
Logistic Map ◽  
Particle Swarm ◽  
Stable Solution ◽  
Optimal Solution ◽  
Cost Functions ◽  
Swarm Optimization ◽  
Chaotic Search ◽  
Modified Particle Swarm Optimization

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

Particle Swarm Optimization for Chemical Multi-Stage and Parallel Batch Scheduling

2014 ◽  
Vol 1037 ◽  
pp. 510-513 ◽  
Author(s):  
Qi Tang ◽  
Peng Liu ◽  
Jian Xun Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Project Scheduling ◽  
Time Window ◽  
Particle Swarm ◽  
Production Capacity ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Optimization Software ◽  
Repair Strategy ◽  
Multi Stage

This paper presents heuristic particle swarm optimization (HPSO) with continuous codes representing the batch production on the machine at each stage to enhance the ability of particle to handle constraints. Repair strategy of particle location is also presented in this paper to meet the demand for products and maximum restriction of production capacity. Based on the analysis of characters of optimal solution, all constraints are unified as time window of batch and solving method for resource-constrained project scheduling with time window is put forward. In order to verify the performance of HPSO, this paper uses random example to conduct test. The results show that HPSO is superior to optimization software and standard PSO.

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