Although there have been many studies on the runtime of evolutionary algorithms in discrete optimization, relatively few theoretical results have been proposed on continuous optimization, such as evolutionary programm...
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Although there have been many studies on the runtime of evolutionary algorithms in discrete optimization, relatively few theoretical results have been proposed on continuous optimization, such as evolutionary programming (EP). This paper proposes an analysis of the runtime of two EP algorithms based on Gaussian and Cauchy mutations, using an absorbing Markov chain. Given a constant variation, we calculate the runtime upper bound of special Gaussianmutation EP and Cauchy mutation EP. Our analysis reveals that the upper bounds are impacted by individual number, problem dimension number n, searching range, and the Lebesgue measure of the optimal neighborhood. Furthermore, we provide conditions whereby the average runtime of the considered EP can be no more than a polynomial of n. The condition is that the Lebesgue measure of the optimal neighborhood is larger than a combinatorial calculation of an exponential and the given polynomial of n.
Multi-objective optimization has been applied in many fields of science, including engineering, economics, finance, and logistics, where optimal decisions need to be taken in the presence of trade-offs between two or ...
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In this study, neuro-fuzzy-based group method of data handling (NF-GMDH) as an adaptive learning network is used to predict the flow discharge in straight compound channels. The NF-GMDH network is developed by using t...
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In this study, neuro-fuzzy-based group method of data handling (NF-GMDH) as an adaptive learning network is used to predict the flow discharge in straight compound channels. The NF-GMDH network is developed by using the particle swarm optimization (PSO) and gravitational search algorithm (GSA). The depth ratio (ratio of water depth in floodplain to that in main channel), coherence parameter, and the discharge ratio [ratio of flow discharge calculated from vertical divided channel method (VDCM) to the bank full discharge] are considered as input parameters to represent a functional relationship between input and output parameters. The performances of training and testing stages for NF-GMDH models were quantified in terms of statistical error parameters. Also, the results of performances were compared with those obtained by using linear genetic programming, nonlinear regression methods, and VDCM. Evaluation of the proposed model demonstrated that NF-GMDH-GSA network provides a more accurate prediction than the NF-GMDH-PSO network. Finally, statistical error parameters indicated that the NF-GMDH networks as a new soft-computing tool produced better prediction of flow discharge in comparison with linear genetic programming, nonlinear regression methods, and VDCM. (C) 2015 American Society of Civil Engineers.
Rocket-based combined cycle (RBCC) engines are an airbreathing propulsion technology that offers considerable potential for efficient access-to-space. Successful design of RBCC-powered space transport systems requires...
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Rocket-based combined cycle (RBCC) engines are an airbreathing propulsion technology that offers considerable potential for efficient access-to-space. Successful design of RBCC-powered space transport systems requires reliable databases for both vehicle and engine performance, calling for an effective sampling method to accurately resolve non-linear characteristics in vast design space. This paper presents an optimal sampling strategy based on the function gradients to realize efficient database construction based on evolutionary algorithms and assesses its effectiveness by applying the methodology to various test functions with multiple objectives as well as surrogate models representing scramjet intake characteristics for validation.
This is a topical issue on the 16th Asia–Pacific Symposium on Intelligent and evolutionary Systems (IES) which was held in Kyoto from December 12–14, 2012. This special issue contains six articles related to evoluti...
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This is a topical issue on the 16th Asia–Pacific Symposium on Intelligent and evolutionary Systems (IES) which was held in Kyoto from December 12–14, 2012. This special issue contains six articles related to evolutionary algorithms that are designed to solve optimization problems, network concepts, mathematical methods and their real world applications.
In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary comp...
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In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation.
Sinai Peninsula represents Egypt's strategic extension and historical link to its Arab neighbors. Owing to its important and critical location, the Egyptian Government budget for the coming year gives priority to ...
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Sinai Peninsula represents Egypt's strategic extension and historical link to its Arab neighbors. Owing to its important and critical location, the Egyptian Government budget for the coming year gives priority to a broad national development project in Sinai. However, the scarcity of freshwater in coastal arid regions of Sinai, coupled with the desired development and the ongoing population growth, makes optimal sustainable water management crucial. This paper is aiming at achieving the optimum groundwater management strategy in a pilot area at Sinai to support the sustainability of the development project. The evolutionary computational algorithms were used to achieve the abovementioned sustainable management strategy. The objective function of the developed management strategy aims to maximize the net benefit from groundwater withdrawal, while minimizing the invasion of saltwater front inside the aquifer. Also, to ensure sustainable development the objective function considers minimizing well interference effects. The results show that the developed framework can be effectively and efficiently used to achieve global solutions of the examined groundwater management problem.
This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Ba...
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This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.
Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this def...
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ISBN:
(纸本)9781450326629
Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this deficiency a bistable dynamic optimisation problem is introduced and the performance of standard evolutionary algorithms and artificial immune systems is assessed. Deviating from the common theoretical perspective that concentrates on the expected time to find a global optimum (again) here the `any time performance' of the algorithms is analysed, i. e., the expected function value at each step. Basis for the analysis is the recently introduced perspective of fixed budget computations. Different dynamic scenarios are considered which are characterised by the length of the stable phases. For each scenario different population sizes are examined. It is shown that the evolutionary algorithms tend to have superior performance in almost all cases.
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