Multi-quantum states quantum-inspired evolutionary algorithm for layout optimization problem and its application

2013 ◽  
Vol 33 (4) ◽  
pp. 1031-1035
Author(s):  
Jiahui MAI ◽  
Renbin XIAO
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Quantum States

Assignment and layout integration optimization for simplified satellite re-entry module component layout

2017 ◽  
Vol 233 (12) ◽  
pp. 4287-4301 ◽  
Author(s):  
Zhi-Zheng Xu ◽  
Chong-Quan Zhong ◽  
Hong-Fei Teng
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Model ◽  
Optimization Problem ◽  
Optimization Method ◽  
Layout Design ◽  
Layout Optimization ◽  
Optimization Process ◽  
Supporting Surface ◽  
Computational Performance ◽  
Component Layout

Previous studies of satellite module component (equipment) layout optimization usually initialized a component assignment in the initialization stage, which kept constant in following optimization process. The invariable component assignment will restrict the further improvement in layout optimization. To overcome this deficiency, an assignment and layout integration optimization method is presented for multi-module or supporting surface satellite module component layout design. The assignment and layout integration optimization model and the component reassignment model are built. The component reassignment model is solved by algorithms with new heuristic rule, and the integration optimization model itself is solved by evolutionary algorithm. The purpose of this article is to improve the computational performance of algorithms for multi-module or supporting surface satellite module component layout optimization. The proposed method is applied to a simplified satellite re-entry module component layout optimization problem to illustrate its effectiveness.

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.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

Two Stage Mini-Max Algorithm for Grid-Based Wind Farm Layout Optimization

2017 ◽  
Author(s):  
Ning Quan ◽  
Harrison Kim
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Discrete Optimization Problem ◽  
Real World Data ◽  
Two Stage ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Grid Based

The power maximizing grid-based wind farm layout optimization problem seeks to determine the layout of a given number of turbines from a grid of possible locations such that wind farm power output is maximized. The problem in general is a nonlinear discrete optimization problem which cannot be solved to optimality, so heuristics must be used. This article proposes a new two stage heuristic that first finds a layout that minimizes the maximum pairwise power loss between any pair of turbines. The initial layout is then changed one turbine at a time to decrease sum of pairwise power losses. The proposed heuristic is compared to the greedy algorithm using real world data collected from a site in Iowa. The results suggest that the proposed heuristic produces layouts with slightly higher power output, but are less robust to changes in the dominant wind direction.

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.

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