Resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances e...
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Resource-constrained project scheduling problems (RCPSPs) represent an important class of practical problems. Over the years, many optimization algorithms for solving them have been proposed, with their performances evaluated using well-established test instances with various levels of complexity. While it is desirable to obtain a high-quality solution and fast rate of convergence from an optimization algorithm, no single one performs well across the entire space of instances. Furthermore, even fora given algorithm, the optimal choice of its operators and control parameters may vary from one problem to another. To deal with this issue, we present a generic framework for solving RCPSPs in which various meta-heuristics, each with multiple search operators, are self-adaptively used during the search process and more emphasis is placed on the better-performing algorithms, and their underlying search operators. To further improve the rate of convergence and introduce good-quality solutions into the population earlier, a local search approach is introduced. The experimental results clearly indicate the capability of the proposed algorithm to attain high-quality results using a small population. Compared with several state-of-the-art algorithms, the proposed one delivers the best solutions for problems with 30 and 60 activities, and is very competitive for those involving 120 activities. (C) 2017 Elsevier Inc. All rights reserved.
In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient...
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In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient non-domination level update (BST-ENLU) approach which uses the basic property of binary search tree. Although the space complexity of BST-ENLU approach is higher than SENLU approach in caseof insertion, but in terms of number of dominance comparisons, BST-ENLU approach can outperform SENLU approach. Thus, these two approaches are complementary to each other. The comparative results show that in case where all the solutions are in different fronts, the maximum number of dominance comparisons using BST-ENLU approach is very less than ENLU approach. A tree based approach is introduced to identify the correct position of the solution to be deleted efficiently. Also a theoretical upper bound to the maximum number of dominance comparisons is obtained for both the proposed approaches in case of both insertion and deletion operations. (C) 2017 Elsevier B.V. All rights reserved.
This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-obj...
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In beamformer design, the microphone configurations which represent microphone number and positions are necessary to be optimized in order to improve the effectiveness of speech enhancement. Determination of microphon...
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In beamformer design, the microphone configurations which represent microphone number and positions are necessary to be optimized in order to improve the effectiveness of speech enhancement. Determination of microphone configuration, number of elements and positions is a nonlinear and non convex NP-hard optimization problem which was not specified before. However, this is a nonlinear and non-convex NP-hard optimization problem. Gradient-based optimization methods can only converge to suboptimal solutions. Although the recently developed heuristic methods may find better configurations, they require long convergence time. In this paper, we study the effectiveness of using Taguchi method to determine microphone configuration. The Taguchi method is a robust and systematic optimization approach for designing reliable and high-quality models. The method conducts systematic trials based on an orthogonal array which represents a subset of representative configurations. It determines the configurations based on the experimental trials, while the heuristic methods determine the configurations by searching through the configuration domain until no better configuration can be found. A case study based on a common office environment is used as an example to illustrate the effectiveness of the Taguchi method and the commonly used heuristic methods. The numerical results demonstrate that the method is capable to develop the microphone configurations with similar performance compared with the heuristic methods when short computational time is only available. Hence, the method is a strong candidate to design microphone configurations when short development time is only available. (C) 2016 Elsevier Ltd. All rights reserved.
Recently, the successful synthesis of two-dimensional (2D) boron on metallic surfaces has motivated great interest in improving the stability of 2D boron to allow the realization of promising materials. Through the us...
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Recently, the successful synthesis of two-dimensional (2D) boron on metallic surfaces has motivated great interest in improving the stability of 2D boron to allow the realization of promising materials. Through the use of an ab initio evolutionary search algorithm, we have discovered a series of TiBx (2 <= x <= 16) structures that consist of earth-abundant titanium and boron atoms in 2D arrangements. These structures are greatly stabilized by electron transfer from Ti to B, therefore, leading to much better stability than the 2D boron sheets proposed so far. In particular, TiB12 has a low enough energy to make it competitive to a mixture of the well-known TiB, compound and a 2D alpha-boron sheet and exhibits a quasi-Dirac point with a 0.02 eV gap. Interestingly, the work function and conductivity of this 2D TiB12 material are calculated to be tunable through the application of biaxial strain. The possibility of synthesis and novel electronic properties expected for 2D TiB12 render it a promising new 2D material for nanoelectronic applications.
Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on ...
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Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint's boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10D and 301) taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms. (C) 2017 Elsevier Ltd. All rights reserved.
A qualitative trial-and-error approach is commonly used to define watershed subdivisions through varying a single topographic threshold value. A methodology has been developed to quantitatively determine spatially var...
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A qualitative trial-and-error approach is commonly used to define watershed subdivisions through varying a single topographic threshold value. A methodology has been developed to quantitatively determine spatially variable threshold values using topography and a user-defined landscape reference layer. Optimization and topographic parameterization algorithms were integrated to create solutions that minimize the number of sub-watersheds and maximize the agreement between the discretized watershed and the reference layer. The system was evaluated using different reference datasets such as soil type, land management, and landscape form. Comparison of simulated results indicated that the scenario using land management as the reference layer yielded results closer to the scenario subdivided using a constant topographic threshold but with approximately 10 times more sub-catchments and therefore indicating customization of the watershed subdivision to the user-defined reference layer. The proposed optimization technology could be used in adequately applying watershed modeling technology in developing conservation practice implementation plans.
Skull-face overlay is the most time-consuming and error-prone stage in craniofacial superimposition, an important skeleton based forensic identification technique. This task focuses on achieving the best possible over...
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Skull-face overlay is the most time-consuming and error-prone stage in craniofacial superimposition, an important skeleton based forensic identification technique. This task focuses on achieving the best possible overlay of an unknown skull found and a single ante-mortem image of a candidate missing person. The process is influenced by some sources of uncertainty since two objects of different nature are involved, i.e. a skull and a face. In previous works we have developed a computer-aided craniofacial superimposition system aimed to assist forensic anthropologists in obtaining the best possible skull and face overlay. The system has successfully allowed us to reduce the processing time, simplify the forensic anthropologists' work, and make the process more objective and reproducible. Our approach is based on automatically overlaying a skull three dimensional model onto a facial photograph by minimizing the distance between two subsets of corresponding cranial and facial landmarks. The proposed method properly deals with the inherent uncertainty sources to the skull face overlay process by considering fuzzy sets to model imprecise landmark location, and imprecise cranial and facial landmarks spatial correspondence (resulting from the presence of soft tissues in the face). Accordingly, our methodology requires computing two kinds of distance metrics: between a point and a fuzzy set, and between two fuzzy sets. This contribution is devoted to study the performance and influence of the most significant and suitable fuzzy distances proposed in the specialized literature, as well as other new ones proposed, on our skull face overlay system. In particular, we have tested the behavior of our automatic method when considering eight different distance measurements. The system performance has been objectively evaluated considering 18 case studies resulting from a ground truth dataset following a rigorous statistical experimental setup. The fact that the choice of a good dist
Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolution...
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Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. An ideal evolutionary algorithm must have efficient exploration in the beginning and enhanced exploitation toward the end. In this paper, a two-phase harmony search (TPHS) algorithm is proposed that attempts to strike a balance between exploration and exploitation by concentrating on diversification in the first phase using catastrophic mutation and then switches to intensification using local search in the second phase. The performance of TPHS is analyzed and compared with 4 state-of-the-art HS variants on all the 30 IEEE CEC 2014 benchmark functions. The numerical results demonstrate the superiority of the proposed TPHS algorithm in terms of accuracy, particularly on multimodal functions when compared with other state-of-the-art HS variants;further comparison with state-of-the-art evolutionary algorithms reveals excellent performance of TPHS on composition functions. Composition functions are combined, rotated, shifted, and biased version of other unimodal and multimodal test functions and mimic the difficulties of real search spaces by providing a massive number of local optima and different shapes for different regions of the search space. The performance of the TPHS algorithm is also evaluated on a real-life problem fromthe field of computer vision called camera calibration problem, ie, a 12-dimensional highly nonlinear optimization problem with several local optima.
In the present research, a decision support tool is presented for the optimization of generation expansion planning (GEP) of semiliberalized electricity markets under specified quantitative energy objectives. The evol...
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In the present research, a decision support tool is presented for the optimization of generation expansion planning (GEP) of semiliberalized electricity markets under specified quantitative energy objectives. The evolution of the anticipated System Marginal Price is estimated overtime, in conjunction with power sector's structure. A hybrid solver has been used for that task. It comprises of the Improved Stochastic Ranking Evolution Strategy (ISRES) and the Interior Point Algorithm (IPA), in an attempt to exploit their distinct characteristics. The quantitative energy objectives are represented as constraints. A relaxation factor is applied on the aforementioned constraints and its effect on the power sector's formation and the optimization procedure is demonstrated through a series of representative computational experiments. The results derived from using various relaxation levels are compared with a base case where the energy constraints are not embedded. Capacity orders are extracted denoting the optimal steps towards accomplishing the energy objectives. The evolution of some core indicators of the energy system is compared amongst the investigated cases. Relaxation facilitated the optimization procedure without intervening in GEP scheduling by much. Moreover, ISRES-IPA is benchmarked against commonly used solvers, demonstrating improved performance on the GEP model's optimization. The decision support tool presented and the conclusions extracted from this study may be of interest to energy policy makers, planners, and investors.
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