scholarly journals Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Sangwook Lee
Keyword(s):  
Genetic Algorithm ◽  
Game Theory ◽  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Bargaining Game ◽  
The Other ◽  
Artificial Agents ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Game Performance

Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC) in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent) participating in the bargaining game: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

scholarly journals A comparative study of particle swarm optimization and genetic algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

scholarly journals Genetic Algorithm and Particle Swarm Optimization Techniques for Inverted Pendulum Stabilization

2020 ◽  
Vol 9 (6) ◽  
pp. 657-660
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Inverted Pendulum ◽  
Biological Evolution ◽  
Particle Swarm ◽  
Continuous Variable ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Soft Computing Techniques

Inverted Pendulum is a popular non-linear, unstable control problem where implementation of stabilizing the pole angle deviation, along with cart positioning is done by using novel control strategies. Soft computing techniques are applied for getting optimal results. The evolutionary computation forms the key research area for adaptation and optimization. The approach of finding optimal or near optimal solutions to the problem is based on natural evolution in evolutionary computation. The genetic algorithm is a method based on biological evolution and natural selection for solving both constrained and unconstrained problems. Particle swarm optimization is a stochastic search method inspired by collective behavior of animals like flocking of birds, schooling of fishes, swarming of bees etc. that is suited to continuous variable problems. These methods are applied to the inverted pendulum problem and their performance studied.

Evolutionary Computation Techniques for Community Detection in Social Network Analysis

2016 ◽  
pp. 266-284 ◽  
Author(s):  
Abhishek Garg ◽  
Anupam Biswas ◽  
Bhaskar Biswas
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Network Analysis ◽  
Evolutionary Computation ◽  
Community Detection ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Computation Techniques ◽  
Growing Networks ◽  
Primary Focus

Community detection is a topic of great interest in complex network analysis. The basic problem is to identify closely connected groups of nodes (i.e. the communities) from the networks of various objects represented in the form of a graph. Often, the problem is expressed as an optimization problem, where popular optimization techniques such as evolutionary computation techniques are utilized. The importance of these approaches is increasing for efficient community detection with the rapidly growing networks. The primary focus of this chapter is to study the applicability of such techniques for community detection. Our study includes the utilization of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) with their numerous variants developed specifically for community detection. We have discussed several issues related to community detection, GA, PSO and the major hurdles faced during the implication of evolutionary approaches. In addition, the chapter also includes a detailed study of how these issues are being tackled with the various developments happening in the domain.

Bacterial Foraging Algorithm Based on PSO with Adaptive Inertia Weigh for Solving Nonlinear Equations Systems

2013 ◽  
Vol 655-657 ◽  
pp. 940-947 ◽  
Author(s):  
Xiong Fa Mai ◽  
Ling Li
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Equations ◽  
Particle Swarm Optimization Algorithm ◽  
The Other ◽  
Systems Of Nonlinear Equations ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Algorithm (BFA) has recently emerged as a very powerful technique for optimization,but it also confronts the problems of slow convergence and premature convergence. To overcome the drawbacks of BFA, This article merge the idea of particle swarm optimization algorithm with adaptive inertia weigh into the bacterial foraging to improve the speed and convergence capabilities of BFA, and according to this a bacterial foraging algorithm based on PSO(APSO-BFA) is presented. Simulation results on five systems of nonlinear equations show that the proposed algorithm is superior to the other two kinds of bacterial foraging algorithm

A Comparative Study of Particle Swarm Optimization and Genetic Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 33-45
Author(s):  
Nazrin Hasanova
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms:  Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

scholarly journals SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM

2015 ◽  
Vol 3 (5) ◽  
pp. 1-12
Author(s):  
Prateek Shrivastava ◽  
Khemraj Deshmukh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Optimization Technique ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Cell Assignment

Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromosomes having gene where each gene act as a block of one solution. This is totally based on the solution which is followed by crossover and then mutation and finally reaches to fitness. The best fitness will be considered as a result and implemented in the practical area. Due to some drawbacks and problems exist in the genetic algorithm implemented, scientists moved to the other algorithm technique which is apparently based on the flock of birds moving to the target. This effectively overcome the shortcomings of GA and provides better fitness solutions to implement in the circuit.

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