A greedy particle swarm optimization (GPSO) algorithm for testing real-world smart card applications

2018 ◽  
Vol 22 (2) ◽  
pp. 183-194
Author(s):  
Hamzeh M. Allawi ◽  
Waref Al Manaseer ◽  
Mohammad Al Shraideh
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Card ◽  
Real World ◽  
Particle Swarm ◽  
Swarm Optimization

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.

scholarly journals Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

2017 ◽  
Author(s):  
Adithya Sagar ◽  
Rachel LeCover ◽  
Christine Shoemaker ◽  
Jeffrey Varner
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Coagulation Cascade ◽  
Benchmark Problems ◽  
Test Functions ◽  
Swarm Optimization ◽  
Particle Swarms

AbstractBackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.ResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed trials with function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.ConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

An Overview of Mutation Strategies in Particle Swarm Optimization

2020 ◽  
Vol 11 (4) ◽  
pp. 16-37
Author(s):  
Waqas Haider Bangyal ◽  
Jamil Ahmad ◽  
Hafiz Tayyab Rauf
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Comprehensive Overview ◽  
Swarm Optimization ◽  
Numerical Experimentation ◽  
Mutation Strategies ◽  
Real World Problems

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

scholarly journals Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Leke Zajmi ◽  
Falah Y. H. Ahmed ◽  
Adam Amril Jaharadak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Real World Problems

With the advancement of Machine Learning, since its beginning and over the last years, a special attention has been given to the Artificial Neural Network. As an inspiration from natural selection of animal groups and human’s neural system, the Artificial Neural Network also known as Neural Networks has become the new computational power which is used for solving real world problems. Neural Networks alone as a concept involve various methods for achieving their success; thus, this review paper describes an overview of such methods called Particle Swarm Optimization, Backpropagation, and Neural Network itself, respectively. A brief explanation of the concepts, history, performances, advantages, and disadvantages is given, followed by the latest researches done on these methods. A description of solutions and applications on various industrial sectors such as Medicine or Information Technology has been provided. The last part briefly discusses the directions, current, and future challenges of Neural Networks towards achieving the highest success rate in solving real world problems.

Bio-Inspired Algorithms for Text Summarization

2017 ◽  
pp. 71-92
Author(s):  
Rasmita Rautray ◽  
Rakesh Chandra Balabantaray
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Real World ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Text Summarization ◽  
Swarm Optimization ◽  
Complex Optimization

In last few decades, Bio-inspired algorithms (BIAs) have gained a significant popularity to handle hard real world and complex optimization problem. The scope and growth of Bio Inspired algorithms explore new application areas and computing opportunities. This paper presents a review with the objective is to bring a better understanding and to motivate the research on BIAs based text summarization. Different techniques have been used for text summarization are genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), harmonic search (HS).

Multi-Objective Particles Swarm Optimization Approaches

2011 ◽  
pp. 20-42 ◽  
Author(s):  
Konstantinos E. Parsopoulos ◽  
Michael N. Vrahatis
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Future Research ◽  
Swarm Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Active Research ◽  
Single Objective

The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm Optimization approaches and applications reported in the literature. This chapter aims at providing a review and discussion of the most established results on this field, as well as exposing the most active research topics that can give initiative for future research.

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