Binary Particle Swarm Optimization Algorithm for Kidney Exchanges Acceleration using Parallel MATLAB

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Kidney Exchange ◽

To Receive

In this paper, we implement a Particle Swarm Optimization (PSO) based method for solving the kidney exchange problem. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Implementing Particle Swarm Optimization in solving the kidney exchange problem is an effective method because the obtained results indicate that PSO outperforms other stochastic-based methods such as Genetic Algorithm regarding the efficient the number of resulting exchanges.

An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization

10.26686/wgtn.14337128 ◽

2021 ◽

Author(s):

Jianhua Liu ◽

Yi Mei ◽

Xiaodong Li

Keyword(s):

Particle Swarm ◽

Continuous Optimization ◽

Swarm Optimization ◽

Inertia Weight ◽

Binary Case ◽

Personal Use ◽

Research Findings ◽

In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling its search capability. There have been intensive studies of the inertia weight in continuous optimization, but little attention has been paid to the binary case. This paper comprehensively investigates the effect of the inertia weight on the performance of binary PSO (BPSO), from both theoretical and empirical perspectives. A mathematical model is proposed to analyze the behavior of BPSO, based on which several lemmas and theorems on the effect of the inertia weight are derived. Our research findings suggest that in the binary case, a smaller inertia weight enhances the exploration capability while a larger inertia weight encourages exploitation. Consequently, this paper proposes a new adaptive inertia weight scheme for BPSO. This scheme allows the search process to start first with exploration and gradually move toward exploitation by linearly increasing the inertia weight. The experimental results on 0/1 knapsack problems show that the BPSO with the new increasing inertia weight scheme performs significantly better than that with the conventional decreasing and constant inertia weight schemes. This paper verifies the efficacy of increasing inertia weight in BPSO. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Solving RFID Networks Scheduling Problems Using Hybird Binary Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.966 ◽

2010 ◽

Vol 29-32 ◽

pp. 966-972 ◽

Cited By ~ 3

Author(s):

Jun Tang

Keyword(s):

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Scheduling Problems ◽

Swarm Optimization ◽

Rfid Reader ◽

Rfid Networks ◽

This paper presents an hybrid binary particle swarm optimization algorithm integration of genetic algorithms (HBPSO) to solve the RFID networks scheduling problems and get the optimal scheduling result in the problem. HBPSO is combined with advantages of binary PSO and GA. We use HBPSO to solve three problems of the RFID reader network and we attempt to minimize the total transaction time. By the results of the three problems, we can conclude that HBPSO is an effective algorithm which can find optimal solutions in the problem of the RFID reader network.

Different Transfer Functions for Binary Particle Swarm Optimization with a New Encoding Scheme for Discounted {0-1} Knapsack Problem

Mathematical Problems in Engineering ◽

10.1155/2021/2864607 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Tung Khac Truong

Keyword(s):

Knapsack Problem ◽

Transfer Functions ◽

Binary Vector ◽

Group Structure ◽

Particle Swarm ◽

Swarm Optimization ◽

Encoding Scheme ◽

The discounted {0-1} knapsack problem (DKP01) is a kind of knapsack problem with group structure and discount relationships among items. It is more challenging than the classical 0-1 knapsack problem. In this paper, we study binary particle swarm optimization (PSO) algorithms with different transfer functions and a new encoding scheme for DKP01. An effective binary vector with shorter length is used to represent a solution for new binary PSO algorithms. Eight transfer functions are used to design binary PSO algorithms for DKP01. A new repair operator is developed to handle isolation solution while improving its quality. Finally, we conducted extensive experiments on four groups of 40 instances using our proposed approaches. The experience results show that the proposed algorithms outperform the previous algorithms named FirEGA and SecEGA . Overall, the proposed algorithms with a new encoding scheme represent a potential approach for solving the DKP01.

An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization

10.26686/wgtn.14337128.v1 ◽

2021 ◽

Author(s):

Jianhua Liu ◽

Yi Mei ◽

Xiaodong Li

Keyword(s):

Particle Swarm ◽

Continuous Optimization ◽

Swarm Optimization ◽

Inertia Weight ◽

Binary Case ◽

Personal Use ◽

Research Findings ◽

In particle swarm optimization (PSO), the inertia weight is an important parameter for controlling its search capability. There have been intensive studies of the inertia weight in continuous optimization, but little attention has been paid to the binary case. This paper comprehensively investigates the effect of the inertia weight on the performance of binary PSO (BPSO), from both theoretical and empirical perspectives. A mathematical model is proposed to analyze the behavior of BPSO, based on which several lemmas and theorems on the effect of the inertia weight are derived. Our research findings suggest that in the binary case, a smaller inertia weight enhances the exploration capability while a larger inertia weight encourages exploitation. Consequently, this paper proposes a new adaptive inertia weight scheme for BPSO. This scheme allows the search process to start first with exploration and gradually move toward exploitation by linearly increasing the inertia weight. The experimental results on 0/1 knapsack problems show that the BPSO with the new increasing inertia weight scheme performs significantly better than that with the conventional decreasing and constant inertia weight schemes. This paper verifies the efficacy of increasing inertia weight in BPSO. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.