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A multi-objective evolutionary programming approach, to the 'object location' spatial analysis and optimisation problem within the urban water management domain

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2005年第2期22卷 85-107页

作者： Makropoulos, CK Butler, D Univ London Imperial Coll Sci Technol & Med Dept Civil & Environm Engn Urban Water Res Grp London SW7 2AZ England

evolutionary programming (EP) is an application of the concepts of Darwinian evolution to complex optimisation problems. This is primarily addressed in the literature through the use of genetic algorithms (GAs), but there are problems where a hybrid approach coupling the robustness of GAs with the effectiveness of a heuristic procedure may yield better results. This paper focuses on the development and use of such a hybrid EP algorithm to solve a particular multi-objective spatial object-location problem, The domain knowledge which forms part of the heuristics of the methodology developed is provided by the problem of citing sustainable water management strategies within the urban Fabric, taking into account social, economic, technical and cost parameters and constraints.

关键词： evolutionary programming location-allocation multi-objective optimisation spatial analysis urban water

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A Multiobjective evolutionary programming Algorithm and Its Applications to Power Generation Expansion Planning

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2009年第5期39卷 1086-1096页

作者： Ceciliano Meza, Jose L. Yildirim, Mehmet Bayram Masud, Abu S. M. Inst Invest Elect Cuernavaca 62490 Morelos Mexico Wichita State Univ Dept Ind & Mfg Engn Wichita KS 67260 USA

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

关键词： Analytical hierarchy process (AHP) evolutionary programming generation expansion planning (GEP) multicriteria optimization operations research optimization methods power generation planning transmission expansion planning

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Application of evolutionary programming to transient and subtransient parameter estimation

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IEEE TRANSACTIONS ON ENERGY CONVERSION 1996年第3期11卷 523-529页

作者： Lai, LL Ma, JT Department of Electrical Electronic and Information Engineering Energy Systems Group City University London UK

This paper presents an artificial intelligence approach of using evolutionary programming to estimate the transient and subtransient parameters of a generator under normal operation, The estimation using evolutionary programming is compared with that using corrected extended Kalman filter. The comparisons with both simulation and micromachine test results show that evolutionary programming is robust to search the real values of parameters even when the data are highly contaminated by noises, while with extended Kalman filter, the estimation tends to diverge with such data.

关键词： electrical machines identification and modelling artificial intelligence evolutionary programming

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Immune-Commensal-evolutionary programming for solving non-smooth/non-convex economic dispatch problem

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ENERGY REPORTS 2020年 6卷 266-275页

作者： Mansor, Mohd Helmi Musirin, Ismail Othman, Muhammad Murtadha Zamani, Mohamad Khairuzzaman Mohamad Shaaya, Sharifah Azwa Jelani, Shahrizal Univ Tenaga Nas Coll Engn Dept Elect & Elect Engn Kajang 43000 Selangor Malaysia Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia

This paper presents the development of a new hybrid optimization technique termed as Immune-Commensal-evolutionary programming (ICEP) and its implementation to solve non-smooth/ non-convex Economic Dispatch (ED) problem. ICEP is developed with the objective to overcome the drawbacks of single optimization techniques like evolutionary programming (EP), Artificial Immune System (AIS) and Symbiotic Organisms Search (SOS). The idea of developing this ICEP technique is to gather the strengths from the three single optimization techniques: EP, AIS and SOS to form a new hybrid technique that can solve non-smooth/non-convex ED problem accurately. This hybrid technique has better performance in finding the global optima of non-smooth/non-convex ED problem compared to the single optimization techniques. The typical drawback of the single optimization techniques is immature convergence, especially EP and AIS techniques. ICEP can avoid this from happening by thoroughly directing the searching process to its global optima. The proposed ICEP technique has been tested on the IEEE 30-Bus Reliability Test System (RTS) and IEEE 57-Bus Reliability Test System (RTS) with three case studies. It is found that ICEP is superior than EP and AIS in producing better non-smooth/non-convex ED solution. (C) 2020 TheAuthor(s). Published by Elsevier Ltd.

关键词： Economic dispatch evolutionary programming Artificial immune system Symbiotic organism search Valve-point loading effect Non-smooth Non-convex

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A solution for unit commitment using Lagrangian relaxation combined with evolutionary programming

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ELECTRIC POWER SYSTEMS RESEARCH 1999年第1期51卷 71-77页

作者： Duo, HZ Sasaki, H Nagata, T Fujita, H Hiroshima Univ Dept Elect Engn Higashihiroshima 7398527 Japan

This paper proposes an approach which combines Lagrangian relaxation principle and evolutionary programming for short-term thermal unit commitment. Unit commitment is a complex combinatorial optimization problem which is difficult to be solved for large-scale power systems. Up to now, the Lagrangian relaxation is considered the best to deal with large-scale unit commitment although it cannot guarantee the optimal solution. In this paper, an evolutionary programming algorithm is used to improve a solution obtained by the Lagrangian relaxation method: Lagrangian relaxation gives the starting point for a evolutionary programming procedure. The proposed algorithm takes the advantages of both methods and therefore it can search a better solution within short computation time. Numerical simulations have been carried out on two test systems of 30 and 90 thermal units power systems over a 24-hour periods. (C) 1999 Elsevier Science S.A. All rights reserved.

关键词： unit commitment Lagrangian relaxation evolutionary programming combinatorial optimization

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Determination of SRMC with non-smooth fuel cost functions using evolutionary programming

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2005年第1期33卷 85-102页

作者： Gnanadass, R Palanivelu, TG Indian Inst Technol Dept Elect Engn Roorkee 247667 Uttar Pradesh India Pondicherry Engn Coll Dept Elect Engn Pondicherry India

In this article, a new algorithm for determination of short run marginal cost (SRMC) for feasible bilateral transactions using optimal power flow (OPF) solution has been presented. Determination of SRMC using conventional methods suffer due to the presence of non-smooth fuel cost generators in the modern, deregulated utilities. Hence in this article, evolutionary programming (EP) based OPF solution has been developed for obtaining optimal generator settings with four non-smooth fuel functions. System transmission loss and penalty factor at each and every bus are computed using the OPF solution. Further bus incremental cost at all the buses is computed using penalty factors. Generalized loss coefficients are also obtained from OPF solution and they are the functions of the system operating state with non-smooth fuel functions. The performance of the proposed algorithm has been validated with IEEE 30 bus and Indian utility 62-bus practical test systems.

关键词： bus incremental cost evolutionary programming non-smooth functions SRMC

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An improved FCMBP fuzzy clustering method based on evolutionary programming

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2011年第4期61卷 1129-1144页

作者： Tan, Qing He, Qing Zhao, Weizhong Shi, Zhongzhi Lee, E. S. Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Chinese Acad Sci Grad Sch Beijing 100049 Peoples R China Kansas State Univ Dept Ind Engn Manhattan KS 66506 USA

In current PC computing environment, the fuzzy clustering method based on perturbation (FCMBP) is failed when dealing with similar matrices whose orders are higher than tens. The reason is that the traversal process adopted in FCMBP is exponential complexity. This paper treated the process of finding fuzzy equivalent matrices with smallest error from an optimization point of view and proposed an improved FCMBP fuzzy clustering method based on evolutionary programming. The method seeks the optimal fuzzy equivalent matrix which is nearest to the given fuzzy similar matrix by evolving a population of candidate solutions over a number of generations. A new population is formed from an existing population through the use of a mutation operator. Better solutions survive into next generation and finally the globally optimal fuzzy equivalent matrix could be obtained or approximately obtained. Compared with FCMBP, the improved method has the following advantages: (1) Traversal searching is avoided by introducing an evolutionary programming based optimization technique. (2) For low-order matrices, the method has much better efficiency in finding the globally optimal fuzzy equivalent matrix. (3) Matrices with hundreds of orders could be managed. The method could quickly get a more accurate solution than that obtained by the transitive closure method and higher precision requirement could be achieved by further iterations. And the method is adaptable for matrices of higher order. (4) The method is robust and not sensitive to parameters. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy clustering FCMBP fuzzy clustering Optimal fuzzy equivalent matrix evolutionary programming

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A hybrid evolutionary programming approach for optimal worst case tolerance design of magnetic devices

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APPLIED SOFT COMPUTING 2012年第8期12卷 2425-2434页

作者： Salcedo-Sanz, Sancho Perez-Bellido, Angel M. Ortiz-Garcia, Emilio G. Portilla-Figueras, Jose A. Jimenez-Fernandez, Silvia Univ Alcala de Henares Dept Signal Theory & Commun Alcala De Henares 28871 Spain

This paper presents a hybrid evolutionary programming approach to solve the worst case tolerance design problem (WCTDP) in magnetic devices. The hybrid algorithm is formed by a basic evolutionary programming approach, mixed with a gradient-guided local search. Two different local searches procedures are tested in the paper, both specially designed to be effective in the WCTDP. Simulations on an example in the design of a magnetic circuit and comparison with several existing bio-inspired heuristics are carried out, and have shown the goodness of our algorithm. (C) 2012 Elsevier B. V. All rights reserved.

关键词： evolutionary programming Hybrid algorithms Tolerance analysis Tolerance design

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Ab-initio process synthesis using evolutionary programming

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CHEMICAL ENGINEERING SCIENCE 2018年 185卷 209-221页

作者： Neveux, Thibaut Univ Lorraine LRGP UMR CNRS 7274 F-54001 Nancy France

Process synthesis methods enable the determination of unit operations and their interconnection into a process flowsheet, with associated design and operating parameters, and responding to given objectives. Modern methods are optimization-based, using for example Mixed Integer Non-Linear programming (MINLP) formulation to optimize a process superstructure. Finding an adequate definition of the search space is a non-trivial problem in such approaches, especially when the number of possible combinations is high due to the process complexity, and is mostly driven by expertise (e.g. heuristics). Consequently, an inductive bias is intrinsically introduced due to restriction of a limited search space, such as the choice of a superstructure representing a limited set of process alternatives. In this work, an evolutionary method is proposed to generate several process architectures based on a set of available unit operations (and associated models) as elementary building blocks. The procedure is here called ab initio process synthesis since it does not require any pre-defined process structure. The developed method relies on the use of an evolutionary programming (EP), mimicking natural evolution at species-level, for the automatic construction of a process by using mutation operators to choose, assemble and connect elementary building blocks (i.e. unit operations). A Non-Linear programming (NLP) is used for process evaluation, by simultaneously solving balances and optimizing process degrees of freedom. The method is implemented in a newly developed tool called PSEvo (Process Synthesis by Evolution). An application to a typical reaction-separation problem is presented, using various problem definitions and evolution control parameters, which demonstrates the method capability to generate optimal processes. The possible uses and the challenges of ab initio process synthesis are finally discussed. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Process systems engineering Process synthesis evolutionary programming Optimization Reaction-separation

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Mathematical modeling of multiple tour multiple traveling salesman problem using evolutionary programming

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APPLIED MATHEMATICAL MODELLING 2015年第12期39卷 3410-3433页

作者： Kota, L. Jarmai, K. Univ Miskolc Dept Mat Handling & Logist H-3515 Miskolc Hungary

This study describes a single phase algorithm for the fixed destination multi-depot multiple traveling salesman problem with multiple tours (mdmTSP). This problem widely appears in the field of logistics mostly in connection with maintenance networks. The general model of the technical inspection and maintenance systems is shown in the first part, where the solution of this problem is an important question. A mathematical model of the system's object expert assignment is proposed with the constraints typical of the system, like experts' capacity minimum and maximum and constraints on maximum and daily tours of the experts. In the second part, the developed evolutionary programming algorithm is described which solves the assignment, regarding the constraints introducing penalty functions in the algorithm. In the last part of the paper, the convergence of the algorithm and the run times and some examination of the parallelization are presented. (C) 2014 Elsevier Inc. All rights reserved.

关键词： evolutionary programming Heuristics Logistics Maintenance networks

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