scholarly journals An Evolutionary Multiagent Framework for Multiobjective Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Zihui Zhang ◽  
Qiaomei Han ◽  
Yanqiang Li ◽  
Yong Wang ◽  
Yanjun Shi
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Layout Optimization ◽  
Layout Problem ◽  
System Decomposition ◽  
System Memory ◽  
Satellite Equipment ◽  
Complete Solutions ◽  
Evolutionary Mode

This article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO-EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem.

A dual-system cooperative co-evolutionary algorithm for satellite equipment layout optimization

2017 ◽  
Vol 232 (13) ◽  
pp. 2432-2457 ◽  
Author(s):  
Feng-Zhe Cui ◽  
Zhi-Zheng Xu ◽  
Xiu-Kun Wang ◽  
Chong-Quan Zhong ◽  
Hong-Fei Teng
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Dual System ◽  
Coordination Mechanism ◽  
Matrix Analysis ◽  
Computational Accuracy ◽  
System Decomposition ◽  
Bearing Plate ◽  
Satellite Equipment

This paper develops a new dual-system cooperative co-evolutionary algorithm for multi-modules (or multi-bearing plate) satellite equipment layout optimization problem, based upon the Potter’s cooperative co-evolutionary framework. Firstly, a new dual-system framework based on the Potter’s cooperative co-evolutionary is constructed and then, corresponding system decomposition rule, matrix analysis method and coordination mechanism are presented. Finally, the way of matching algorithms (e.g. evolutionary algorithms and swarm intelligence algorithms) with systems A and B in the dual system is presented. The purpose is to enhance the computational accuracy and robustness of the developed algorithm for satellite equipment layout optimization problem. The experimental results show that the developed algorithm has better computational accuracy and robustness (computational success ratio and standard deviation) as compared with four dual-system algorithms and two single-system algorithms based upon Potter’s cooperative co-evolutionary.

Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie
Keyword(s):  
Optimization Design ◽  
Pareto Front ◽  
Layout Optimization ◽  
Multiple Objective ◽  
Multi Objective Optimization ◽  
Layout Problem ◽  
Basic Principles ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

scholarly journals Multiobjective optimization design of the solar field and reverse osmosis system with preheating feed water using Genetic algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 624-642 ◽  
Author(s):  
Iman Ebrahimi Ghoujdi ◽  
Hasti Hadiannasab ◽  
Mokhtar Bidi ◽  
Abbas Naeimi ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Reverse Osmosis ◽  
Optimization Design ◽  
Feed Water ◽  
Solar Field
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