Analyzing Genetic Algorithm with Game Theory and Adjusted Crossover Approach on Engineering Problems

2015 ◽  
pp. 177-188 ◽  
Author(s):  
Edson Koiti Kudo Yasojima ◽  
Roberto Célio Limão de Oliveira ◽  
Otávio Noura Teixeira ◽  
Rodrigo Lisbôa ◽  
Marco Mollinetti
Keyword(s):  
Genetic Algorithm ◽  
Game Theory ◽  
Engineering Problems

AN OVERVIEW ON GAME THEORY APPLICATIONS TO ENGINEERING

2013 ◽  
Vol 15 (03) ◽  
pp. 1340019 ◽  
Author(s):  
JOAQUIN SANCHEZ-SORIANO
Keyword(s):  
Game Theory ◽  
State Of The Art ◽  
Point Of View ◽  
Sole Purpose ◽  
Engineering Problems ◽  
Theoretic Point ◽  
Game Theoretic

In this paper, we review some of the literature in which different applications to engineering problems are analyzed from a game-theoretic point of view. The revision is far from exhaustive and the sole purpose of this paper is to provide an approximate state-of-the-art on this topic. Likewise, we try throughout the paper to highlight what game theory could contribute to the study of engineering problems.

EXPERIENCE OF THE GAME THEORY APPLICATION IN CONSTRUCTION MANAGEMENT / LOŠIMŲ TEORIJOS TAIKYMO PATIRTIS STATYBOS VADYBOJE

2008 ◽  
Vol 14 (4) ◽  
pp. 531-545 ◽  
Author(s):  
Friedel Peldschus
Keyword(s):  
Game Theory ◽  
Solution Strategies ◽  
Conflict Situations ◽  
Engineering Problems ◽  
The Game Theory ◽  
Potential Applications ◽  
Fuzzy Games ◽  
Theory Application ◽  
Construction Operation ◽  
Zero Sum

The game theory allows mathematical solutions of conflict situations. Besides the fairly established application to economical problems, approaches to problems in construction operation have been worked out. An overview of applications is given. Solution strategies for such engineering problems are collected. Furthermore, concrete application examples are presented and an overview of further potential applications is given. Solutions of two‐person zero‐sum games are discussed as well as approaches to fuzzy games. Santrauka Lošimų teorija teikia matematinių sprendimų konfliktinėse situacijose. Straipsnyje pateikta daug ekonominių problemų sprendimo pavyzdžių, sukurtų statybos valdymo problemų sprendimo metodikų. Atlikta šių tyrimų apžvalga, surinktos minėtų inžinerinių problemų sprendimo strategijos. Pateikiami konkretūs teorijos taikymo pavyzdžiai dabarties sąlygomis ir ateityje. Aptariami „dviejų asmenų nulinės sumos“ lošimų sprendiniai, taip pat neapibrėžtų aibių teorijos taikymo lošimuose atveju.

Evaluated Preference Genetic Algorithm and its Engineering Applications

2011 ◽  
Vol 467-469 ◽  
pp. 2129-2136
Author(s):  
Tung Kuan Liu ◽  
Hsin Yuan Chang ◽  
Wen Ping Wu ◽  
Chiu Hung Chen ◽  
Min Rong Ho
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Computation Complexity ◽  
Genetic Population ◽  
Multiobjective Genetic Algorithm ◽  
Multiobjective Optimization Problems ◽  
Engineering Problems ◽  
Objective Vector

This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.

scholarly journals Using particle swarm optimization to solve test functions problems

2021 ◽  
Vol 10 (6) ◽  
pp. 3422-3431
Author(s):  
Issa Ahmed Abed ◽  
May Mohammed Ali ◽  
Afrah Abood Abdul Kadhim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Speed Of Convergence ◽  
Test Functions ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Engineering Problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

Parametric Analysis of Iterated Game Environments as Social Interaction Model for Genetic Algorithm to Solve Constrained Engineering Problems

2018 ◽  
Author(s):  
Rodrigo Lisboa Pereira ◽  
Marco A. Florenzano Mollinetti ◽  
Mario Tasso Ribeiro Serra Neto ◽  
Adilson de Almeida Neto ◽  
Daniel Leal Souza ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Social Interaction ◽  
Parametric Analysis ◽  
Interaction Model ◽  
Engineering Problems ◽  
Game Environments

A Game Theory Approach for Multiple Design Tasks Schedule

2010 ◽  
Vol 26-28 ◽  
pp. 163-166
Author(s):  
Guo Hai Zhang ◽  
Guang Hui Zhou ◽  
Xue Qun Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Game Theory ◽  
Solution Algorithm ◽  
Optimal Scheduling ◽  
Theory Approach ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
Numerical Case Study

This paper presents a new kind of scheduling solution for multiple design tasks in networked developing environments. The main contributions of this study can be focused on three points: The first is to distinguish the concepts and contents of the task scheduling in the networked developing environments. The second is to construct a game-theory mathematical model to deal with this new multiple design tasks scheduling problem. In the presented mathematical model, the players, strategies and payoff are given separately. Therefore, obtaining the optimal scheduling results is determined by the Nash equilibrium (NE) point of this game. In order to find the NE point, a genetic algorithm (GA)-based solution algorithm to solve this mathematical model is proposed. Finally, a numerical case study is presented to demonstrate the feasibility of the methods.

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