scholarly journals Entropy-Driven Global Best Selection in Particle Swarm Optimization for Many-Objective Software Package Restructuring

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amarjeet Prajapati ◽  
Anshu Parashar ◽  
Sunita ◽  
Alok Mishra
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Software Package ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Selection Strategy ◽  
Swarm Optimization ◽  
Selection Strategies ◽  
Conflicting Objectives ◽  
Selection Of

Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems (MaOPs) often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization (MaOPSO) has been proposed. However, external archive maintenance and selection of leaders for designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy (called EMPSO) to solve the many-objective software package restructuring (MaOSPR) problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches.

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.

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.

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.

Particle Swarm Optimization

2015 ◽  
Vol 2 (1) ◽  
pp. 54-87 ◽  
Author(s):  
Manoj Kumar ◽  
Jyoti Raman ◽  
Priya Priya
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Process Models ◽  
Surface Grinding ◽  
Grinding Process ◽  
Network Systems ◽  
Swarm Optimization ◽  
Conflicting Objectives

In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize parameters in surface grinding processes where multiple conflicting objectives are present. The relationships between surface grinding process parameters and the performance measures of interest are obtained by using experimental data and particle swarm optimization intelligent neural network systems (PSOINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex surface grinding operations optimization problems. In this paper the key grinding process models and relationships that were discovered by previous research efforts have been unified in the form of a particle swarm optimization intelligent neural network systems.

A New Particle Swarm Optimization and Differential Evolution Technique for Constrained Optimization Problems

2013 ◽  
Author(s):  
Dhafar Al-Ani ◽  
Saeid Habibi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Constrained Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Test Problems ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Multi Objective

Real-world problems are often complex and may need to deal with constrained optimization problems (COPs). This has led to a growing interest in optimization techniques that involve more than one objective function to be simultaneously optimized. Accordingly, at the end of the multi-objective optimization process, there will be more than one solution to be considered. This enables a trade-off of high-quality solutions and provides options to the decision-maker to choose a solution based on qualitative preferences. Particle Swarm Optimization (PSO) algorithms are increasingly being used to solve NP-hard and constrained optimization problems that involve multi-objective mathematical representations by finding accurate and robust solutions. PSOs are currently used in many real-world applications, including (but not limited to) medical diagnosis, image processing, speech recognition, chemical reactor, weather forecasting, system identification, reactive power control, stock exchange market, and economic power generation. In this paper, a new version of Multi-objective PSO and Differential Evolution (MOPSO-DE) is proposed to solve constrained optimization problems (COPs). As presented in this paper, the proposed MOPSO-DE scheme incorporates a new leader(s) updating mechanism that is invoked when the system is under the risk of converging to premature solutions, parallel islands mechanism, adaptive mutation, and then integrated to the DE in order to update the particles’ best position in the search-space. A series of experiments are conducted using 12 well-known benchmark test problems collected from the 2006 IEEE Congress on Evolutionary Computation (CEC2006) to verify the feasibility, performance, and effectiveness of the proposed MOPSO-DE algorithm. The simulation results show the proposed MOPSO-DE is highly competitive and is able to obtain the optimal solutions for the all test problems.

Multiobjective Optimization of a Containership Using Deterministic Particle Swarm Optimization

2007 ◽  
Vol 51 (03) ◽  
pp. 217-228 ◽  
Author(s):  
Antonio Pinto ◽  
Daniele Peri ◽  
Emilio F. Campana
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Test Problems ◽  
Stochastic Algorithm ◽  
Design Problems ◽  
Swarm Optimization ◽  
New Strategy ◽  
Selection Of

The purpose of this paper is to show how the improvement of the hydrodynamics performance of a ship can be obtained by solving a shape optimization problem using the particle swarm optimization (PSO) technique. PSO has been recently introduced to solve global optimization problems and belongs to the class of evolutionary algorithms. In this paper, the basic stochastic algorithm is modified into a deterministic method, eliminating the randomized heuristic search. This algorithm has been then extended to deal with multiobjective problems by following the concept of subswarms and introducing a new strategy for the selection of the subswarm leaders. Two different versions of this strategy are illustrated and compared. Effectiveness and efficiency of the method proposed here are demonstrated by solving a set of algebraic multiobjective test problems, designed to represent a wide selection of possible shapes of the Pareto front. Comparisons with a well-known multiobjective genetic algorithm are also presented. Finally, the new method is used to reduce the heave and pitch motion peaks of the response amplitude operator of a containership advancing at fixed speed in head seas, subject to some real-life constraints. The results confirm the applicability of the developed approach to real ship design problems.

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

2007 ◽  
Vol 221 (3) ◽  
pp. 177-191 ◽  
Author(s):  
M. K. Pandey ◽  
M. K. Tiwari ◽  
M. J. Zuo
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Reliability Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New Variant ◽  
Conflicting Objectives ◽  
Enhanced Particle Swarm Optimization

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.

An improved quantum based particle swarm optimizer applied to electromagnetic optimization problems

2018 ◽  
Vol 37 (1) ◽  
pp. 319-332
Author(s):  
Obaid Ur Rehman ◽  
Shiyou Yang ◽  
Shafiullah Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Stochastic Methods ◽  
Selection Strategy ◽  
Swarm Optimization ◽  
Content Type ◽  
Quantum Particle Swarm Optimization ◽  
Number Of Iterations

Purpose The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems. Design/methodology/approach A modified quantum particle swarm optimization (MQPSO) algorithm is designed. Findings The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated. Originality/value The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

A Study of Single-Objective Particle Swarm Optimization and Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 543-547 ◽  
pp. 1635-1638 ◽  
Author(s):  
Ming Li Song
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simple Realization ◽  
Single Objective ◽  
Selection Of

The complexity of optimization problems encountered in various modeling algorithms makes a selection of a proper optimization vehicle crucial. Developments in particle swarm algorithm since its origin along with its benefits and drawbacks are mainly discussed as particle swarm optimization provides a simple realization mechanism and high convergence speed. We discuss several developments for single-objective case problem and multi-objective case problem.

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