This paper describes the use of a numerical optimization method based on an evolutionary local search algorithm for obtaining the wavefront aberration from a real interferogram. By finding the near-optimal solution to...
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Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider t...
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Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for a hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.
evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward ...
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evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward the achievement of the common goal. The mutation operation is responsible for diversity maintenance, while the selection operation favors the survival of the fittest. In this paper we focus our attention on the crossover operator. The crossover operator by default is responsible for the search effort and as such deserves our special attention. In particular, we propose a two-stage crossover (TSX) operator for more efficient exploration of the search space. The performance of the proposed TSX operator is assessed in comparison with the simulated binary crossover operator with the assistance of three well-known multiobjective evolutionary algorithms, namely the NSGAII, the SPEA2 and the MOCELL, for the solution of the DTLZ1-7 set of test functions. We also compare the proposed TSX with other popular reproduction operators like the differential evolution and the particle swarm optimization. Finally, we examine the efficacy of the TSX operator in handling problems having five objectives. It is shown with the assistance of the Deb, Thiele, Laumanns and Zitzler set of test functions that the TSX operator can substantially improve the results generated by three popular performance metrics for most of the cases.
Many systems or applications have been developed for distributed environments with the goal of attaining multiple objectives in the face of environmental challenges such as high dynamics/hostility or severe resource c...
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Many systems or applications have been developed for distributed environments with the goal of attaining multiple objectives in the face of environmental challenges such as high dynamics/hostility or severe resource constraints (e.g., energy or communications bandwidth). Often the multiple objectives are conflicting with each other, requiring optimal tradeoff analyses between the objectives. This paper is mainly concerned with how to model multiple objectives of a system and how to optimize their performance. We first conduct a comprehensive survey of the state-of-the-art modeling and solution techniques to solve multi-objective optimization problems. In addition, we discuss pros and cons of each modeling and optimization technique for in-depth understanding. Further, we classify existing approaches based on the types of objectives and investigate main problem domains, critical tradeoffs, and key techniques used in each class. We discuss the overall trends of the existing techniques in terms of application domains, objectives, and techniques. Further, we discuss challenging issues based on the inherent nature of multi-objective optimization problems. Finally, we suggest future work directions in terms of what critical design factors should be considered to design and analyze a system with multiple objectives.
Both cooling and thermal insulation can be achieved using a triple-glazed skylight filled with a gas that absorbs and emits thermal radiation. Utilizing radiative cooling as the driving force a cooling effect can be a...
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Both cooling and thermal insulation can be achieved using a triple-glazed skylight filled with a gas that absorbs and emits thermal radiation. Utilizing radiative cooling as the driving force a cooling effect can be achieved in the here outlined skylight, while it can also act as a thermal insulator when desired. Inside the skylight the gas it contains circulates by natural convection induced by its heated lower compartment to the radiatively cooled upper compartment that "sees" the sky. By this circulation, cooling is obtained to a room located below the skylight. When this circulation is cut off, the skylight acts as a thermal insulator. This gives a multi-objective optimization problem, as these two objectives are conflicting. In this article, a Pareto front is created that shows possible trade-off solutions between the cooling and insulating properties of the designed skylight. This Pareto front is created by optimizing the dimensions of the skylight. Thus, the skylight will provide an optimal cooling effect and an optimal insulation effect when needed.
An adaptive differential evolution algorithm with an aging leader and challengers mechanism, called ADE-ALC, is proposed to solve optimization problems. In ADE-ALC algorithm, the aging mechanism is introduced into the...
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An adaptive differential evolution algorithm with an aging leader and challengers mechanism, called ADE-ALC, is proposed to solve optimization problems. In ADE-ALC algorithm, the aging mechanism is introduced into the framework of differential evolution to maintain diversity of the population. The key control parameters are adaptively updated based on given probability distributions which could learn from their successful experiences to generate the promising parameters at the next generation. One of the two local search operators is randomly selected to generate challengers which are beneficial for increasing the diversity of population. Finally, the effectiveness of the ADE-ALC algorithm is verified by the numerical results of twenty-five benchmark test functions. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, an extension of the graph colouring problem is introduced to model a parallel machine scheduling problem with job incompatibility. To get closer to real-world applications, where the number of machines ...
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In this paper, an extension of the graph colouring problem is introduced to model a parallel machine scheduling problem with job incompatibility. To get closer to real-world applications, where the number of machines is limited and jobs have different processing times, each vertex of the graph requires multiple colours and the number of vertices with the same colour is bounded. In addition, several objectives related to scheduling are considered: makespan, number of pre-emptions and summation over the jobs' throughput times. Different solution methods are proposed, namely, two greedy heuristics, two tabu search methods and an adaptive memory algorithm. The latter uses multiple recombination operators, each one being designed for optimising a subset of objectives. The most appropriate operator is selected dynamically at each iteration, depending on its past performance. Experiments show that the proposed algorithm is effective and robust, while providing high-quality solutions on benchmark instances for the graph multi-colouring problem, a simplification of the considered problem.
Support vector regression models are powerful surrogates used in various fields of engineering. Due to the quality of their predictions and their efficiency, those models are considered as a suitable tool for surrogat...
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Support vector regression models are powerful surrogates used in various fields of engineering. Due to the quality of their predictions and their efficiency, those models are considered as a suitable tool for surrogate evaluation. Despite their advantages, support vector regression models require an accurate selection of the configuration parameters in order to achieve good generalization performance. To overcome this limitation, a new hyperparameter selection method is developed. This method takes into account the training error to identify the optimal parameters set using evolutionary optimization schemes. Moreover, building on state-of-the-art techniques, an alternative analytically-assisted genetic algorithm is proposed in order to enhance the accuracy and robustness of the optimization scheme. The configuration is elaborated from a new search strategy in the design space. The results verify that the proposed technique improve the prediction accuracy and its robustness. Several test cases are used to demonstrate the capabilities of the method and its application potential to real engineering problems. The results prove that a surrogate model coupled with this adaptive configuration technique provides a useful prediction model suitable for various types of numerical experiments. (C) 2017 Elsevier B.V. All rights reserved.
The wire electro-discharge machining technique is used in many operations, from the simplest cutting applications to the manufacturing of complex and delicate parts. Costs and the variety of wire used in the cutting p...
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The wire electro-discharge machining technique is used in many operations, from the simplest cutting applications to the manufacturing of complex and delicate parts. Costs and the variety of wire used in the cutting process vary according to the particular workpiece to be cut. The literature on this subject features studies conducted on the surface roughness and the metal removal rate. However, the surface sensitivity in the manufacturing of cutting dies is not a crucial property. The most important variable, rather, is the cost involved in making simple cutting dies. In this study, a Bohler K100 workpiece and CuZn37 wire material has been used to find the optimal conditions for high quality surface roughness, maximizing metal removal rate and minimizing wire consumption. Furthermore, the metal removal rate related directly to the cutting speed was calculated and compared with the consumption. To facilitate calculation of wire consumption, the evolutionary programming module was used for modeling and derivation of the formula for calculation with 99 % accuracy.
In the present study, a design of biologically inspired computing framework is presented for solving second-order two-point boundary value problems (BVPs) by differential evolution (DE) algorithm employing finite diff...
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In the present study, a design of biologically inspired computing framework is presented for solving second-order two-point boundary value problems (BVPs) by differential evolution (DE) algorithm employing finite difference-based cost function. The DE has been implemented to minimize the combined residue from all nodes in a least square sense. The proposed methodology has been evaluated using five numerical examples in linear and nonlinear regime of BVPs in order to demonstrate the process and check the efficacy of the implementation. The assessment and validation of the DE algorithm have been carried out by comparing the DE-computed results with exact solution as well as with the corresponding data obtained using continuous genetic algorithms. These benchmark comparisons clearly establish DE as a competitive solver in this domain in terms of computational competence and precision.
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