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Structural design of four-component optically compensated zoom lenses: Use of evolutionary programming

OPTIK 2012年第17期123卷 1534-1541页

作者： Pal, S. Hazra, L. N. Univ Calcutta Dept Appl Opt & Photon Kolkata India

A new approach for 'ab initio' synthesis of thin lens structure of optically compensated zoom lenses is reported. This is accomplished by an implementation of evolutionary programming that explores the available configuration space formed by powers of individual components and inter-component separations to obtain globally or quasiglobally optimum solutions for the problem. Normalization of the variables is carried out to get an insight on the optimum structures. The method has been successfully used to get thin lens structures of optically compensated zoom lens systems by suitable formulation of merit function of optimization. Investigations have been carried out on four-component zoom lens structures. Illustrative numerical results are presented. (C) 2011 Elsevier GmbH. All rights reserved.

关键词： Optically compensated zoom lens Zoom lens evolutionary programming Genetic algorithm Structural design Thin lens design

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Multi-thread evolutionary programming and its application to truck-and-trailer backer-upper control

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2001年第2期E84A卷 597-603页

作者： Hong, CS Won, JM Lee, JS Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea

This paper presents a multi-thread evolutionary programming (MEP) technique that is composed of global, local, and minimal search units. An appropriate starch routine is called depending on the current situation and the individuals are updated by using the selected routine. In each search routine, the individuals are updated with a normalized relative fitness function to improve the robustness of the algorithm. The proposed method is applied to the problem of backing up a truck-and-trailer system to a loading dock. A fuzzy logic controller is designed for a truck-and-trailer backer-upper system and the MEP algorithm is used to optimize the representative parameters of the fuzzy logic controller. The simulation results show that the proposed controller performs well even under a large variety of initial positions.

关键词： evolutionary programming parameter optimization truck-and-trailer backer-upper system

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A modification to classical evolutionary programming by shifting strategy parameters

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APPLIED INTELLIGENCE 2013年第2期38卷 175-192页

作者： Alipouri, Yousef Poshtan, Javad Alipouri, Yagub Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Civil Engn Tehran Iran

Many methods have been recently suggested for promoting the performance of evolutionary programming (EP) in finding the optimum point of functions or applications. EP has some shortcomings that slow down its convergence to the global minimum, especially for multimodal functions. As it is known, mutation is one of the most important operators in EP, which produces new attributes in variables. Mutation must be kept under control;otherwise, it destroys heritage information. In EP, mutation is implemented by adding strategy parameters to variable vectors of parents to produce offspring. When one of the strategy parameters is a large value, adding it to the related variable causes abrupt changes in that variable. Thus, the variable grows with large steps and deviates far from the optimum point, whereas some of the other variables do not sense considerable changes. If this event continues for more iterations, the variable will go further. This event slows down EP in some iterations. To avoid such an occurrence, this paper introduces a new method that can overcome these disadvantages and enhance the performance of classical evolutionary programming. This paper describes a modification of evolutionary programming by using a rotational method to prevent large and small changes to the strategy parameters. This method adds one function to the mutation operator. This function operates on strategy parameters and changes the sequence of these parameters. Because this method does not directly operate on variables, it will not destroy the heritage information of the parents. This method was tested on fifty well-known test functions used in the literature and was compared with nine well-known EP variants. The results are robust and demonstrate the efficiency of the technique.

关键词： evolutionary programming Shifting operator Strategy parameters Step size Fixed and adaptive lower bound strategy parameter

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Self-adaptive evolutionary programming and its application to multi-objective optimal operation of power systems

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ELECTRIC POWER SYSTEMS RESEARCH 2001年第3期57卷 181-187页

作者： Shi, LB Xu, GY Chongqing Univ Coll Elect Engn Chongqing 400044 Peoples R China

This paper proposes a new algorithm to solve multi-objective optimal operation of power systems problem. The algorithm is based on combination of general evolutionary programming and random search technique. The algorithm includes two important procedures. First, a new pattern of mutation is developed in this paper. Secondly, the developed mutation operator is Self-adaptive during optimization. Furthermore, in a multi-objective optimal operation study four objectives (cost of generation with valve point loading, transmission losses, environmental pollution and steady-state security regions) are considered for optimization, and an ideal point method is used to solve the problem. The proposed algorithm is tested on the IEEE six-bus and 30-bus systems. Numerical results and comparison demonstrate that the new method not only can deal agilely with constraints, but also can reduce the CPU time and prevent the search from being in local optima. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： evolutionary programming mutation multi-objective planning operation of power systems

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Hybrid evolutionary programming using adaptive Levy mutation and modified Nelder-Mead method

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SOFT COMPUTING 2019年第17期23卷 7913-7939页

作者： Pang, Jinwei He, Jun Dong, Hongbin Harbin Engn Univ Dept Comp Sci & Technol Harbin Heilongjiang Peoples R China Nottingham Trent Univ Sch Sci & Technol Clifton Campus Nottingham NG11 8NS England

evolutionary programming has been widely applied to solve global optimization problems. Its performance is related to both mutation operators and fitness landscapes. In order to make evolutionary programming more efficient, its mutation operator should adapt to fitness landscapes. The paper presents novel hybrid evolutionary programming with adaptive Levy mutation, in which the shape parameter of Levy probability distribution adapts to the roughness of local fitness landscapes. Furthermore, a modified Nelder-Mead method is added to evolutionary programming for enhancing its exploitation ability. The proposed algorithm is tested on 39 selected benchmark functions and also benchmark functions in CEC2005 and CEC2017. The experimental results demonstrate that the overall performance of the proposed algorithm is better than other algorithms in terms of the solution accuracy.

关键词： Global optimization evolutionary programming Fitness landscape Levy distribution Nelder-Mead method Algorithm design

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Application of evolutionary programming to optimal operational strategy cogeneration system under time-of-use rates

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2000年第5期22卷 367-373页

作者： Tsay, MT Lin, WM Cheng Shiu Inst Technol Dept Elect Engn Kaohsiung Taiwan

This paper presents the application of evolutionary programming (EP) to optimal operational strategy of cogeneration systems under Time-of-Use (TOU) rare. The fuel consumption and steam generation will first be measured and the Input-Output (I/O) curve derived using the regression method. The operational model developed also considers the connection of the cogeneration system with the utility company in terms of TOU rate and various fuel consumptions. EP was adopted to decide the optimal fuel dispatch, steam output of boiler, and generation output subjective to satisfying all the operation constraints. The Newton-Raphson based method has been implemented to show that EP does have the tendency of getting the global optimum. The proposed methodology could provide a practical model for both the utility company and the cogeneration industry to follow. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： cogeneration system evolutionary programming time-of-use

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An evolutionary programming-based tabu search method for solving the unit commitment problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2004年第1期19卷 577-585页

作者： Rajan, CCA Mohan, MR Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Anna Univ Sch Elect Engn Madras 600025 Tamil Nadu India

This paper presents a new approach to solving the short-term unit commitment problem using an evolutionary programming-based tabu search (TS) method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all of the units according to their initial status ("flat start"). Here, the parents are obtained from a predefined set of solutions (i.e., each and every solution is adjusted to meet the requirements). Then, a random decommitment is carried out with respect to the unit's minimum downtimes, and TS improves the status by avoiding entrapment in local minima. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different power systems consisting of 10, 26, and 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation, and simulated annealing and tabu search in reaching proper unit commitment.

关键词： dynamic programming evolutionary programming Lagrangian relaxation Tabu search unit commitment

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A hybrid multi-objective evolutionary programming-Firefly Algorithm for different type of Distributed Generation in distribution system

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ENERGY REPORTS 2022年 8卷 169-174页

作者： Husni, Noor Najwa Husnaini Mohammad Rahim, Siti Rafidah Abdul Adzman, Mohd Rafi Hussain, Muhamad Hatta Musirin, Ismail Azmi, Syahrul Ashikin Univ Malaysia Perlis Fac Elect Engn Technol Arau 02600 Perlis Malaysia Univ Malaysia Perlis CERE Arau 02600 Perlis Malaysia Univ Teknol MARA UiTM Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia

With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective evolutionary programming-Firefly Algorithm (MOEPFA) technique is presented in this study for solving multi-objective power system problems which are minimizing total active and reactive power losses and improving voltage profile while considering the cost of energy losses. This MOEPFA is developed by embedding Firefly Algorithm (FA) features into the conventional EP method. The analysis in this study considered DG with 4 different scenarios. Scenario 1 is the base case or without DG, scenario 2 is for DG with injected active power, scenario 3 is for DG injected with reactive power only and scenario 4 is for DG injected with both active and reactive power. The IEEE 69-bus test system is applied to validate the suggested technique. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Distributed Generation Multi-objective optimization Loss minimization evolutionary programming

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Available transfer capability assessment using evolutionary programming based capacity benefit margin

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2006年第3期28卷 166-176页

作者： Othman, MM Mohamed, A Hussain, A Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

The determination of ATC must accommodate a reasonable range of capacity benefit margin (CBM) so that the operation of power system is secure from the generation deficiency that may occur during a power transfer. There are two ways of incorporating CBM into ATC, which are by considering the CBM as firm and non-firm transfers. The CBM for each area is specified based on the installed generation capacity that gives the loss-of-load expectation index below 2.4 h/year. The determination of CBM based on heuristic search for a large size power system with many areas is complicated and computationally time consuming. Therefore, a new technique is proposed which uses evolutionary programming (EP) to maximize the total amount of generation capacity so as to determine the CBM for each area. The EP performance is improved by using the modified Gaussian formulation in which it has the capability of providing a new population in a fast global maximum domain search. The proposed EP with modified Gaussian formulation in estimating the CBM is verified on the modified 24 bus IEEE reliable test system. Comparison in terms of accuracy and computation time in estimating the CBM is made by considering the four methods which are the EP using modified Gaussian formulation, EP using standard Gaussian formulation, genetic algorithm (GA) and the conventional method. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： available transfer capability capacity benefit margin evolutionary programming genetic algorithm

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Performance comparison of evolutionary programming and differential evolution approaches for social welfare maximization by placement of multi type FACTS devices in pool electricity market

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 67卷 517-528页

作者： Balamurugan, K. Muralisachithanandam, R. Dharmalingam, V. SASTRA Univ Dept Elect & Elect Engn Thanjavur Tamil Nadu India MAM Sch Engn Tiruchirappalli Tamil Nadu India

In restructuring power markets, the money matters of demand and supply governs the prices at each bus with rigid constraints imposed in transmission line. The market operators can misuse the "market power" and make additional profit. It eventually hinders the growth of restructured power market and minimizes the social welfare. Hence Flexible AC Transmission System (FACTS) devices are considered one such technology that helps in improving the social welfare. In this paper, the new methodology for proper placement of FACTS devices in the restructured electricity market is proposed. An efficient and reliable evolutionary programming (EP) and differential evolution (DE) techniques based economic dispatch for pool electricity market is used to improve the social welfare. These techniques are developed for both the cases with and without FACTS devices and the results are compared. Utilization of these FACTS devices helps in "maximizing the benefit" and "minimizing the cost" and also helps in reducing the total number of overloads, excess power flow and severity of overloading. The standard data of sample 5 bus, three generators and two customers system has been taken into account and simulated with aid of MAT-lab software and comparative results are obtained. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Deregulated electricity market Differential evolution evolutionary programming FACTS devices Five bus system Optimal placement

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