A proper balance between exploration and exploitation is essential to maintain adequate genetic diversity within the evolving population of an evolutionary algorithm (EA). Early loss of genetic diversity causes premat...
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ISBN:
(纸本)9781479904006;9781479903979
A proper balance between exploration and exploitation is essential to maintain adequate genetic diversity within the evolving population of an evolutionary algorithm (EA). Early loss of genetic diversity causes premature trapping around the locally optimal points of the fitness landscape. evolutionary programming (EP), one of the major branches of EA, obtains exploration and exploitation abilities by mutation operators. As one single mutation operator is not sufficient, mixing several explorative and exploitative mutation operators can improve the performance of EP. This paper presents a mixed mutation scheme for EP based on a guided selection strategy. This strategy guides the participation of mutation operators throughout the evolutionary process. The proposed algorithm has been examined on a test-suite of 20 benchmark functions. Experimental results show that combining different mutation operators along with the guided selection strategy significantly enhance the performance of EP.
Different mutation operators such as Gaussian, Cauchy and Levy mutations have been proposed in evolutionary programming. According to the no free lunch theorem, operators are only efficient within certain fitness land...
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ISBN:
(纸本)9781509006229
Different mutation operators such as Gaussian, Cauchy and Levy mutations have been proposed in evolutionary programming. According to the no free lunch theorem, operators are only efficient within certain fitness landscapes. Therefore the mixed strategy, integrating several mutation operators into a single algorithm, is a nature development in order to combine the advantages of different operators. Based on Shapley value, this paper presents a new mixed strategy evolutionary programming algorithm. It employs Gaussian, Cauchy and Levy mutation operators and uses Shapley value to assign weights to these three operators. Then evolutionary programming using the new mixed strategy is tested on a set of 22 benchmark problems. The performance of the new mixed strategy is compared with other two mixed mutation strategies and three pure strategies. The experimental results show that the new mixed strategy has achieved an acceptable accuracy.
A new generalized evolutionary programming with Levy-type mutation is proposed. The Levy-type distribution is know to reproduce Gaussian, Cauchy, and Student's t-distributions and characterized by a power-law fat-...
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A new generalized evolutionary programming with Levy-type mutation is proposed. The Levy-type distribution is know to reproduce Gaussian, Cauchy, and Student's t-distributions and characterized by a power-law fat-tail. This new evolutionary programming is tested for five standard test functions. The average performance of the new algorithm with Levy-type mutation for hard optimization problems is superior to the original evolutionary programming with Gaussian mutation. (C) 2002 Elsevier Science B.V. All rights reserved.
Ensuring a smooth electrical energy to the consumer has been identified as the main role of electric supply utility. In doing so, the power utility needs to ensure that the electrical power is generated with minimum c...
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ISBN:
(纸本)9781424402731
Ensuring a smooth electrical energy to the consumer has been identified as the main role of electric supply utility. In doing so, the power utility needs to ensure that the electrical power is generated with minimum cost. Hence, for economic operation of the system, the total demand must be appropriately shared among the generating units with an objective to minimize the total generation cost for the system with the voltage level maintained at the secure operating limit. Dynamic economic dispatch (DED) is one of the main functions of power generation, operation and control. DED is an improvement of the conventional economic dispatch. This paper proposes an optimization technique to solve dynamic economic dispatch (DED) in the electric power system using Ant Colony Optimization (ACO) technique. Prior to the DED scheme, static economic dispatch (SED) was conducted for the purpose of comparison. Implementation on an IEEE test system highlighted the merit of the proposed EP technique for the DED implementation
evolutionary programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measur...
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ISBN:
(纸本)0819428396
evolutionary programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measured by current standards. This paper provides a series of experiments that follow the framework of the original approach from the early 1960s, brought up to date with current computing machinery. A brief review of evolutionary programming and its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies, is also offered.
Economic dispatch is an important issue in electrical power system planning and operation. Its aim is to dispatch electricity to the consumers at minimal economic cost, while satisfying system constraints. Prohibited ...
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ISBN:
(纸本)9781509067756
Economic dispatch is an important issue in electrical power system planning and operation. Its aim is to dispatch electricity to the consumers at minimal economic cost, while satisfying system constraints. Prohibited operating zones is one of the constraints which requires attention. This paper presents an efficient optimization technique termed as Immune Log-Normal evolutionary programming (ILNEP) to solve economic dispatch problem with prohibited operating zones. The proposed ILNEP optimization technique to solve economic dispatch problem with prohibited operating zones has been tested on the IEEE 26-Bus Reliability Test System (RTS). The proposed method has been tested on three case studies with different load conditions, for validation of its effectiveness. The three case studies are lightly loaded, mediumly loaded and heavily loaded. Comparison of results among ILNEP, artificial immune system (AIS) and evolutionary programming (EP) have been made and it revealed that ILNEP outperformed AIS and EP in giving the lowest total production cost.
This paper proposes a hybrid method that integrates the main features of particle swarm optimization (PSO) and evolutionary programming (EP) for solution of non-convex economic load dispatch (ELD) problems having non-...
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ISBN:
(纸本)078038718X
This paper proposes a hybrid method that integrates the main features of particle swarm optimization (PSO) and evolutionary programming (EP) for solution of non-convex economic load dispatch (ELD) problems having non-linearities like valve point loadings. Algorithms based on PSO, evolutionary programming (EP) and PSO embedded EP techniques have been developed and tested on a practical non-convex ELD problem with valve point loading effects considered in the cost functions. Numerical results show that all the algorithms are capable of finding feasible near global solutions within a reasonable time but PSO embedded EP-algorithm with Gaussian mutation appears to outperform the other two in terms of convergence speed, solution time and quality of solution.
A simple approach for controlling a pH neutralisation process has been developed in this paper. This method is applied to a weak acid-strong base neutralisation process. The objective of the control effort is to maint...
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evolutionary programming (EP) seems a promising methodology to automatically find programs to solve new computing challenges. The evolutionary programming techniques use classical genetic operators (selection, crossov...
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ISBN:
(纸本)9780956494405
evolutionary programming (EP) seems a promising methodology to automatically find programs to solve new computing challenges. The evolutionary programming techniques use classical genetic operators (selection, crossover and mutation) to automatically generate programs targeted to solve computing problems or specifications. Among the methodologies related with evolutionary programming we can find Genetic programming, Analytic programming and Grammatical Evolution. In this paper we present the evolutionary programming Multi-agent Systems (EPMAS) framework based on Grammatical Evolution (GE) to evolutionary generate Multi-agent systems (MAS) ad-hoc. We also present two case studies in MAS scenarios for applying our EPMAS framework: the predator-prey problem and the Iterative Prisoner's Dilemma.
This paper describes a new Multi-Objective evolutionary programming (MOEP) method to solve the Combined Economic Emission Dispatch (CEED) and Economic Emission Dispatch (EED) problems. The CEED is a bi-objective optim...
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ISBN:
(纸本)9781424419050
This paper describes a new Multi-Objective evolutionary programming (MOEP) method to solve the Combined Economic Emission Dispatch (CEED) and Economic Emission Dispatch (EED) problems. The CEED is a bi-objective optimization problem that considers two objectives such as fuel cost and NO, emission. It is converted into a single objective optimization problem using weighted sum method. The EED is a three-objective optimization problem that considers the fuel cost, NO, and SO2 emissions as objectives. Non-dominated solution ranking is employed as selection mechanism in the proposed MOEP for the CEED and EED problems. The developed algorithm is tested for a three-unit and a six-unit systems, and six and 30 bus systems. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective problems in a single run.
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