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作者： Šijanov, Denis Charles University of Prague

Title: Using genetic algorithms to create AI tank model controllers Author: Denis Šijanov Department: Department of Software and Computer Science Education Supervisor: Mgr. Jakub Gemrot, Ph.D., Department of Software and Computer Science Education Abstract: Genetic algorithm is a general procedure designed for solving equations that do not have an algorithm or a better way of solving them yet. In addition this process can be explained even by using simple examples, which makes it an interesting exercise for students of computer science. The students may encounter varying forms of publication of these algorithms such as videos or controlled simulations. These forms of presentation may help the subjects understand the basic principles of an algorithm, but they are not interactive. The students therefore cant apply their ideas and find out what impact on the algorithm they would have had. Details such as these are very important when trying to understand genetic algorithms. There are applications which solve user defined problems by using genetic algorithms. They demonstrate the usability of the algorithm, but they do not help the users understand the algorithm. The most efficient way to understand genetic algorithms in my opinion is to write one on your own. This is why I have decided to create an...

关键词： artificial intelligence evolution algorithms tank models battle simulation

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Calibration of rainfall-runoff models using global optimization.

INGENIERIA HIDRAULICA EN MEXICO

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INGENIERIA HIDRAULICA EN MEXICO 2003年第1期18卷 55-73页

作者： Sardiñas, JG Pedreira, MDJ Inst Super Politecn Jose A Echeverria Fac Ingn Civil Dept Matemat Havana 19390 Cuba Inst Super Politecn Jose A Echeverria Fac Ingn Civil Ctr Invest Hidraul Dept Ingn Hidraul Havana 19390 Cuba

Conceptual rainfall-runoff models are difficult to calibrate by means of automatic methods;one major reason for this is the inability of conventional procedures to locate the globally optimal set of parameters. This paper discusses two existing global search procedures: the Shuffled Complex evolution Method (SCE-UA) and the Genetic Algorithm coupled with Sequential Quadratic Programming (GA-SQP). Both are evaluated on the conceptual rainfall-runoff TCG model, applied both to a single basin and to a complex basin. For both types of basin, a theoretical case without model and data errors was considered in which the true value of parameters are known a priori, as well as a real-world case, where model and data errors exist Finally, a new global optimization procedure, entitled The Modified Competitive Complex evolution algorithm (MCCE), is presented. Regarding the single basin, the SCE-UA and MCCE algorithms were the most realiable, while the other one gave solutions equivalent to those of the SCE-UA and MCCE in the theoretical case;but in the real-world case, the GA-SQP showed an increasing tendency to be trapped in local minima. With reference to the complex basin, none of the three algorithms identified the exact solution in the theoretical case. However, the SCE-UA and MCCE algorithms systematically produced a better solution than the GA-SQP algorithm. In the real-world case, their solutions were stable, while the GA-SQP algorithm produced a very unstable set of parameters.

关键词： automatic calibration calibration of rainfall-runoff models evolution algorithms global optimization in rainfall-runoff models

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Automated steel reinforcement detailing in reinforced concrete frames using evolutionary optimization and artificial potential field

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AUTOMATION IN CONSTRUCTION 2022年 138卷 104224-104224页

作者： Xu, Chengran Liu, Jiepeng Wu, Zhou Chen, Y. Frank Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA

Steel reinforcement detailing is critical in the reinforced concrete (RC) frame design of collision-free rebar layouts. This paper presents a methodological framework for automated steel reinforcement detailing. An optimization model is developed to calculate optimal rebar combinations, where material cost and construction convenience are considered. The neighborhood field optimization (NFO) algorithm is applied to obtain the solution, and an integrated artificial potential field (APF) and NFO method is proposed to automatically generate a collision-free rebar layout, where the NFO enables agents to escape from the local minimum point of the APF. The developed framework is applied to two RC frames to determine the diameter, length, and position of each rebar. The results show that the developed framework can rapidly and accurately complete steel reinforcement detailing for various RC frames.

关键词： Reinforced concrete (RC) frames Steel reinforcement Rebar combination Rebar layout evolution algorithms Artificial potential field (APF)

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Hybrid evolutionary Optimization/Heuristic Technique for Water System Expansion and Operation

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第5期142卷 C4015006.1-C4015006.1页

作者： Morley, Mark Tricarico, Carla Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Univ Cassino & Lazio Meridionale Dipartimento Ingn Civile & Meccan Via Di Biasio 43 I-03043 Cassino Italy

This paper presents a methodological solution to The Battle of Background Leakage Assessment for Water Networks (BBLAWN) competition. The methodology employs two constrained multiple-objective optimization problems and is implemented in the context of a software application for the generic hydraulic optimization and benchmarking of water distribution system (WDS) problems. The objectives are the combined infrastructure and operational costs and system-wide leakage, both to be minimized. In order to accelerate the evaluation of potential solutions, a distributed computing approach permits multiple EPANET solutions to be evaluated in parallel. A pressure-driven demand extension to EPANET assists the optimization in accurately ranking near-feasible solutions and to dynamically allocate leakage demand to nodes. Pressure-reducing valves (PRVs) have been located in two ways: a priori, with respect to the optimization analysis, and a posteriori after the infrastructure optimization to reduce excess pressure and pipe leakage. The latter demonstrates better overall fitness, leading to optimal configurations dominating those obtained with the former. Several temporal resolutions for PRV settings have been evaluated to contrast the optimal solutions with the computational effort required. (C) 2015 American Society of Civil Engineers.

关键词： Multiple objective optimization evolution algorithms Battle of Background Leakage Assessment forWater Networks (BBLAWN) Water distribution systems Leakage

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Hybrid Optimization Method for Strategic Control of Water Withdrawal from Water Reservoir with Using Support Vector Machines

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Procedia Engineering 2017年 186卷 491-498页

作者： Pavel Mensik Daniel Marton Brno University of Technology Faculty of Civil Engineering Institute of Landscape and Water Management Veveri 95 Brno 602 00 Czech Republic

The aim of strategic control is the effort to achieve optimal water resource management (water reservoir). Classic strategic control of water withdrawal from water reservoir are based mainly on the rules and rules curves, which are created by generalization of historical data of water inflows to the reservoirs and water demand. Discharge series are changing in the time due to expected climate change. It is necessary to looking for intelligent water withdrawal control, which will allow to react on these hydrological changes and contribute to the efficient use of accumulated water for ensure water demand. The paper will describe the algorithm based on adaptive control. The normal adaptive control required knowledge of the water flow medium-term prediction into the reservoir. The created algorithm of intelligent water withdrawal control does not require knowledge of hydrological predictions. This control method is based on a suitable combination optimization method with the Support vector machines method. The control algorithm is one of the possible measures to mitigate the negative impacts of droughts and water scarcity. The algorithm of adaptive control is applied to the control of water withdrawal from selected single-reservoir. The results are compared with usual rules for water withdrawal control.

关键词： Adaptive evolution algorithms Operative Control Reservoir Strategic Control Optimization Support Vector Machines Water Demand Water Resource Water Withdrawal

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Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control

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Procedia Engineering 2014年 89卷 1486-1492页

作者： P. Menšík M. Starý D. Marton Brno University of Technology Faculty of Civil Engineering Inst. of Landscape and Water Management Brno 602 00 Czech Republic

Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.

关键词： Adaptive evolution algorithms hydropower function large open reservoir operative control optimization predictive model

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A Comparison of Population-based Optimization Techniques for Water Distribution System Expansion and Operation

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Procedia Engineering 2014年 89卷 13-20页

作者： M.S. Morley C. Tricarico Exeter Advanced Analytics LLP 109 Pennsylvania Road Exeter Devon EX4 6DT (United Kingdom) Department of Civil and Mechanical Engineering University of Cassino and Southern Lazio via Di Biasio 43 03043 Cassino (Italy)

This paper presents a water distribution system expansion and operation methodology employing population-based optimization algorithms applied to the Battle of Background Leakage Assessment for Water Networks competition. The problem is formulated as constrained single and multiple-objective optimization problems and implemented in a generic hydraulic optimization and benchmarking software application (Acquamark). To accelerate the evaluation of potential solutions, a distributed computing approach is employed, where necessary, to permit multiple solutions to be executed and evaluated in parallel. A pressure-driven demand extension to the EPANET hydraulic model is also employed to assist the optimization techniques in accurately ranking near-feasible solutions and to dynamically allocate leakage demand to the end nodes of each pipe.

关键词： Multiple Objective Optimization evolution algorithms BBLAWN Water Distribution Systems Leakage

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Application of Genetic algorithms in Stock Market Simulation

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Procedia - Social and Behavioral Sciences 2012年 47卷 93-97页

作者： Jiří Štěpánek Jiří Šťovíček Richard Cimler University of Hradec Králové Rokitanského 62 Hradec Králové 50003 Czech Republic

Development of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application of genetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basic model of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms used in learning process of simulation.

关键词： evolution algorithms genetic programming multiagent simulation stock-market

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Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

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algorithms 2020年第5期13卷 108-108页

作者： Vakhnin, Alexey Sopov, Evgenii Reshetnev Siberian State Univ Sci & Technol Dept Syst Anal & Operat Res Krasnoyarsky Rabochy Ave 31 Krasnoyarsk 660037 Russia Siberian Fed Univ Dept Informat Syst Svobodny Ave 79 Krasnoyarsk 660041 Russia

Many modern real-valued optimization tasks use "black-box" (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of evolutionary Computation provides a special session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the success-history based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the "epsilon-constrained method (namely "epsilon-iCC-SHADE). We investigated the performance of the "-iCC-SHADE and compared it with the previously proposed "epsilon-CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.

关键词： large-scale global optimization evolution algorithms differential evolution cooperative coevolution constrained optimization

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Application Research of Four-Branch Antenna Design by Improved Orthogonal Multi-objective evolutionary Algorithm

Application Research of Four-Branch Antenna Design by Improv...

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The 2011 International Conference on Electric and Electronics(EEIC 2011)

作者： Jincui Guo Xiaojuan Zhao Jinxin Zou Faculty of Mechanical & Electronic Information China University of Geosciences

In this paper, the optimization of four-branch antenna is studied by improved orthogonal multi-objective evolutionary algorithm. The multi-objective evolutionary algorithm can be used to search the large space for the optimal solution, but the orthogonal design method can be used to find a potential good solution which may be located near the proper position of the optimal solution. And the orthogonal design method is a kind of very good sampling method, the method can be used to solve the larger dimension of the dynamic optimization problems, it also improves the performance of the antenna. The NEC software is used as numerical calculation method in this article. The design results of the four-branch antenna show the accuracy and efficiency of the method.

关键词： evolution algorithms orthogonal design multi-objective optimization

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