In the past decades, subsurface non-aqueous phase liquid (NAPL) contamination has been recognized as one of the most widespread and challenging environmental problems. Thus, researchers have focused their efforts on d...
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In the past decades, subsurface non-aqueous phase liquid (NAPL) contamination has been recognized as one of the most widespread and challenging environmental problems. Thus, researchers have focused their efforts on developing and testing the efficiency of remediation methodologies, able to address the unique nature of these contaminants. Recently, in-situ flooding techniques for the accelerated removal of NAPLs trapped in the subsurface have been proposed, where additives are injected together with water upgradient of the NAPL-contaminated area in order to alter the physio-chemical properties of the contaminants, such as interfacial tension, and enhance their solubilities. In this work, the efficiency of ethanol enhanced NAPL remediation is addressed. To this end, a non-linear, multi-objective optimization strategy is developed by combining a multiphase flow simulation model with evolutionary algorithms. Two conflicting optimization objectives are considered: minimizing operation cost and maximizing remediation efficiency, while preventing uncontrolled NAPL mobilization. More specifically, the first objective involves the operation cost of the procedure, which is directly proportional to the pumping rate, duration and ethanol volume used. The second represents the environmental considerations of the problem that, in this work, are described by the maximization of free product removal and the prevention of DNAPL vertical spreading. (C) 2016 The Authors. Published by Elsevier Ltd.
evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective function evaluations instead of leveraging...
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ISBN:
(纸本)9780791850114
evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective function evaluations instead of leveraging domain knowledge to guide the optimization. An evolutionary algorithm's performance can be improved by utilizing operators called domain specific heuristics that incorporate domain knowledge, but existing knowledge-intensive algorithms utilize one or two domain specific heuristics, which limits the amount of incorporated knowledge or treats all knowledge as equally effective. We propose a hyperheuristic approach that efficiently utilizes multiple domain-specific heuristics that incorporate knowledge from different sources by allocating computational resources to the effective ones. Furthermore, a hyperheuristic allows the simultaneous use of conventional evolutionary operators that assist in escaping local optima. This paper empirically demonstrates the efficacy of the proposed hyperheuristic approach on a multi-objective design problem for an Earth observation satellite system. Results show that the hyperheuristic approach significantly improves the search performance compared to an evolutionary algorithm that does not use any domain knowledge.
This paper analyzes the influence of specific genetic operations on the results obtained when applying the Unplugged evolutionary Algorithm in an artistic creation process. Throughout the methodology developed in prev...
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This paper presents an FPGA based synthesizable offline UAV local path planner implementation using evolutionary algorithms for 3D unknown environments. A Genetic Algorithm is selected as the path planning algorithm a...
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Bin packing problems are a class of optimization problems that have numerous applications in the industrial world, ranging from efficient cutting of material to packing various items in a larger container. We consider...
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ISBN:
(纸本)9780889869844
Bin packing problems are a class of optimization problems that have numerous applications in the industrial world, ranging from efficient cutting of material to packing various items in a larger container. We consider here only rectangular items cut off an infinite strip of material as well as off larger sheets of fixed dimensions. This problem has been around for many years and a great number of publications can be found on the subject. Nevertheless, it is often difficult to reconcile a theoretical paper and practical application of it. The present work aims to create simple but, at the same time, fast and efficient algorithms, which would allow one to write high-speed and capable software that can be used in a real-time application.
In this paper, we present a new, computationally inexpensive method for preventing premature convergence in multimodal evolutionary algorithms by population injection. Our method avoids the premature convergence of th...
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Multi-population evolutionary algorithms are, by nature, highly complex and difficult to describe. Even two populations working in concert (or opposition) present a myriad of potential configurations that are often di...
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The past few years have seen several variants of evolutionary algorithms (EAs) applied to solving Sudoku puzzles. Given that EAs with simple components do not work properly, considerable efforts have gone into designi...
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This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied ...
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This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision varia
Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained opt...
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Several speed-up techniques for evolutionary algorithms (EA) are considered in this paper. Our long-term research is oriented towards development of highly accelerated EA for solving large, non-linear, constrained optimization problems. In particular, we briefly discuss here advances in development and samples of numerical analysis for already preliminarily proposed speed-up techniques, including smoothing and balancing, adaptive step-by-step mesh refinement, as well as a'posteriori error analysis and related techniques. Important engineering applications in computational mechanics are planned, including residual stress analysis in railroad rails, and vehicle wheels, as well as a wide class of problems resulting from the Physically Based Approximation of experimental and/or numerical data. The improved EA provides significant speed-up of convergence and/or possibility of solving such large problems, when the standard EA fails.
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