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.
In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter ...
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ISBN:
(纸本)9781479948741
In this work, considering the solution of analog filter approximation problem, evolutionary algorithms are used to obtain nth order transfer functions. Coefficients of denominator polynomial of low pass analog filter are optimized and this process is carried out for three different orders of transfer functions. Simulation results show that error values obtained with evolutionary algorithms are less than that of traditional methods. The feasibility of the proposed method on circuit realization is investigated by designing passive and active analog filter circuits which realize 3rd order transfer function.
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passban...
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ISBN:
(纸本)9781479930203
In this work, the denominator coefficients of a low-pass filter transfer function are optimized with evolutionary algorithms in order to obtain minimum approximation error and to reduce the distortion over the passband and stopband separately. For each design case, three different orders of transfer function are optimized. Simulation results show that evolutionary algorithms used in this work results in a short computation time with less approximation error than the conventional methods. Passive and active circuit realizations of filter transfer functions obtained with the most efficient EA method are also provided in order to show the feasibility of the proposed approach for circuit implementation.
In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic alg...
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In a preference-based multi-objective optimization task, the goal is to find a subset of the Pareto-optimal set close to a supplied set of aspiration points. The reference point based non-dominated sorting genetic algorithm (R-NSGA-II) was proposed for such problem-solving tasks. R-NSGA-II aims to finding Pareto-optimal points close, in the sense of Euclidean distance in the objective space, to the supplied aspiration points, instead of finding the entire Pareto-optimal set. In this paper, R-NSGA-II method is modified using recently proposed Karush-Kuhn-Tucker proximity measure (KKTPM) and achievement scalarization function (ASF) metrics, instead of Euclidean distance metric. While a distance measure may not produce desired solutions, KKTPM-based distance measure allows a theoretically-convergent local or global Pareto solutions satisfying KKT optimality conditions and the ASF measure allows Pareto-compliant solutions to be found. A new technique for calculating KKTPM measure of a solution in the presence of an aspiration point is developed in this paper. The proposed modified R-NSGA-II methods are able to solve as many as 10-objective problems as effectively or better than the existing R-NSGA-II algorithm.
The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due...
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Wood-plastic composites (WPCs) have emerged as a sustainable and cost-effective material for construction, particularly in low-cost housing solutions. However, designing WPC panels that meet structural, serviceability...
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Wood-plastic composites (WPCs) have emerged as a sustainable and cost-effective material for construction, particularly in low-cost housing solutions. However, designing WPC panels that meet structural, serviceability, and manufacturing constraints remains a challenge. This study focused on optimizing the cross-sectional shape of WPC roof panels using evolutionary algorithms to minimize material usage while ensuring compliance with deflection and stress constraints. Two evolutionary algorithms-the genetic algorithm (GA) and particle swarm optimization (PSO)-were employed to optimize sinusoidal and trapezoidal panel profiles. The optimization framework integrated finite element analysis (FEA) to evaluate structural performance under uniformly distributed loads and self-weight. The modulus of elasticity of the WPC material was determined experimentally through three-point bending tests, ensuring accurate material representation in the simulations. The trapezoidal profile proved to be the most optimal, exhibiting superior deflection performance compared with the sinusoidal profile. A comparative analysis of GA and PSO revealed that PSO outperformed GA in both solution optimality and convergence speed, demonstrating its superior efficiency in navigating the design space and identifying high-performance solutions. The findings highlight the potential of WPCs in low-cost housing applications and offer insights into the selection of optimization algorithms for similar engineering design problems.
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