In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a No...
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In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a Nonlinear Optimal Control Problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time. Four evolutionary based path planning methods namely Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Differential Evolution (DE), and Firefly Algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analyze the performance and capability of the mentioned methods for guiding an AUV from an initial loitering point toward the rendezvous through a comprehensive simulation study. The proposed planner module entails a heuristic for refining the path considering situational awareness of environment, encompassing static and dynamic obstacles within a spatiotemporal current fields. The planner thus needs to accommodate the unforeseen changes in the operating field such as emergence of unpredicted obstacles or variability of current field and turbulent regions. The simulation results demonstrate the inherent robustness and efficiency of the proposed planner for enhancing a vehicle's autonomy so as to enable it to reach the desired rendezvous. The advantages and shortcoming of all utilized methods are also presented based on the obtained results. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environ...
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In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang-Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms.
Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Var...
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Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.
The runtime of an evolutionary algorithm can be reduced by increasing the number of parallel evaluations. However, increasing the number of parallel evaluations can also result in wasted computational effort since the...
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The runtime of an evolutionary algorithm can be reduced by increasing the number of parallel evaluations. However, increasing the number of parallel evaluations can also result in wasted computational effort since there is a greater probability of creating solutions that do not contribute to convergence towards the global optimum. A trade-off, therefore, arises between the runtime and computational effort for different levels of parallelization of an evolutionary algorithm. When the computational effort is translated into cost, the trade-off can be restated as runtime versus cost. This trade-off is particularly relevant for cloud computing environments where the computing resources can be exactly matched to the level of parallelization of the algorithm, and the cost is proportional to the runtime and how many instances that are used. This paper empirically investigates this trade-off for two different evolutionary algorithms, NSGA-II and differential evolution (DE) when applied to a multi-objective discrete-event simulation (DES) problem. Both generational and steady-state asynchronous versions of both algorithms are included. The approach is to perform parameter tuning on a simplified version of the DES model. A subset of the best configurations from each tuning experiment is then evaluated on a cloud computing platform. The results indicate that, for the included DES problem, the steady-state asynchronous version of each algorithm provides a better runtime versus cost trade-off than the generational versions and that DE outperforms NSGA-II.
The profitability of the livestock industry largely depends on cost-effective feed ration formulation as feed accounts for between 60 and 80% of production costs. Therefore, feed formulation is a recurring problem for...
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The profitability of the livestock industry largely depends on cost-effective feed ration formulation as feed accounts for between 60 and 80% of production costs. Therefore, feed formulation is a recurring problem for breeders. In addition, the presence of linear and non-linear constraints, and multiple possible combinations that are subject to upsurge makes the formulation of feed a Non-deterministic Polynomial-time hard (NP-hard) problem. Generally, feed formulation is done by specifying the nutritional requirements as rigid constraints and an algorithm attempts to find a feasible cost-effective formulation. However, relaxing the constraints can sometimes provide a huge reduction in the cost of feed while not seriously affecting the economic performance of the livestock. This entails the development of a feed formulation software that has an inbuilt mechanism to enable relaxation to the constraints based on the users' necessities. Accordingly, the problem formulation and the optimization algorithm should facilitate this. We modified the conventional problem formulation with a tolerance parameter (as a percentage of the actual value) to accommodate the relaxation of constraints. We solved this problem with differential evolution, a variant of evolutionary algorithms, which are good for handling NP-hard problems. In addition, the relaxation of the constraints was done in an interactive way using the proposed method without penalties. In other words, the proposed method is flexible and possesses the ability to search for a feasible and least-cost solution if available or otherwise, the best solution and finds the suitable feed components to be used in ration formulation at an optimal cost depending on the nutrient requirements and growth stage of the animal.
The deployment of an unmanned aerial network (UAV-network) for the optimal coverage of ground nodes is an NP-hard problem. This work focuses on the application of a multi-layout multi-subpopulation genetic algorithm (...
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The deployment of an unmanned aerial network (UAV-network) for the optimal coverage of ground nodes is an NP-hard problem. This work focuses on the application of a multi-layout multi-subpopulation genetic algorithm (MLMPGA) to solve multi-objective coverage problems of UAV-networks. The multi objective deployment is based on a weighted fitness function that takes into account coverage, fault tolerance, and redundancy as relevant factors to optimally place the UAVs. The proposed approach takes advantage of different subpopulations evolving with different layouts. This feature is aimed at reflecting the evolutionary concept of different species adapting to the search space conditions of the multi-objective coverage problem better than single-population genetic algorithms. The proposed multi-subpopulation genetic algorithm is evaluated and compared against single-population genetic algorithm configurations and other well-known meta-heuristic optimization algorithms, such as particle swarm optimization and hill climbing algorithm, under different numbers of ground nodes. The proposed MLMPGA achieves significantly better performance results than the other meta-heuristic algorithms, such as classical genetic algorithms, hill climbing algorithm, and particle swarm optimization, in the vast majority of our simulation scenarios. (C) 2017 Published by Elsevier B.V.
Freight transportation is important for the national economy in many countries. An efficient distribution of products within supply chains may lower the associated costs and allow setting competitive prices to increas...
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Freight transportation is important for the national economy in many countries. An efficient distribution of products within supply chains may lower the associated costs and allow setting competitive prices to increase the number of sales. Many supply chain players use the cross-docking terminals to facilitate the cargo distribution process. An effective scheduling of the arriving trucks at the cross-docking terminals is critical to ensure their timely service. A number of evolutionary algorithms have been developed to solve the truck scheduling problem, some of which apply strong mutation for altering solutions throughout the search process, while the rest rely on weak mutation without providing any justification for applying a specific mutation mechanism. This study performs a comprehensive comparative analysis of the strong and weak mutation mechanisms. Furthermore, a novel heuristic algorithm, which accounts for the truck service priority and the truck service order restrictions, is proposed for initializing the chromosomes and population. The truck scheduling problem at a cross-docking terminal is formulated as a mixed integer programming model, minimizing the total weighted truck service cost. An evolutionary Algorithm is designed to solve the problem. Two categories of the evolutionary Algorithm, one of which applies strong mutation, while the other one relies on weak mutation, are evaluated based on various performance indicators. Results demonstrate that deployment of weak mutation improves the objective function value at termination on average by 10.8% as compared with strong mutation without affecting the computational time substantially. The analysis also shows that weak mutation yields more diverse population. Moreover, the proposed heuristic for initializing the chromosomes and population outperforms the initialization mechanisms that are commonly used in the literature.
Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test len...
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Context evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many specific aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the choice of evolutionary algorithm has to date seen less attention in the literature. Objective: Since it is theoretically impossible to design an algorithm that is the best on all possible problems, a common approach in software engineering problems is to first try the most common algorithm, a genetic algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. The objective of this paper is to perform this analysis, in order to shed light on the influence of the search algorithm applied for unit test generation. Method: We empirically evaluate thirteen different evolutionary algorithms and two random approaches on a selection of non-trivial open source classes. All algorithms are implemented in the Evosuite test generation tool, which includes recent optimisations such as the use of an archive during the search and optimisation for multiple coverage criteria. Results: Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the DynaMOSA many-objective search algorithm is the most effective algorithm for unit test generation. Conclusion: Our results show that the choice of algorithm can have a substantial influence on the performance of whole test suite optimisation. Although we can make a recommendation on which algorithm to use in practice, no algorithm is clearly superior in all cases, suggesting future work on improved search algorithms for unit test generation.
Context: evolutionary algorithms typically require large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) ...
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Context: evolutionary algorithms typically require large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods. Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach. We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms. Results: Using just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms. Conclusion: Just because something works, and is widespread use, does not necessarily mean that there is no value in seeking methods to improve that method. Before undertaking search-based SE optimization tasks using traditional EAs, it is recommended to try other techniques, like those explored here, to obtain the same results with fewer evaluations. (C) 2017 Elsevier B.V. All rights reserved.
evolutionary optimization algorithms by imitating survival of the best features and transmutation of the creatures within their generation, approach complicated engineering problems very well. Similar to many other fi...
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evolutionary optimization algorithms by imitating survival of the best features and transmutation of the creatures within their generation, approach complicated engineering problems very well. Similar to many other field of research, civil engineering problems have benefited from this capacity. In the current study, optimum design of retaining walls under seismic loading case is analyzed by three evolutionary algorithms, differential evolution (DE), evolutionary strategy (ES), and biogeography-based optimization algorithms (BBO). All the results are benchmarked with the classical evolutionary algorithm, genetic algorithm (GA). To this end, two different measures, minimum-cost and minimum-weight, are considered based on ACI 318-05 requirements coupled with geotechnical considerations for retaining walls. Numerical simulations on three case studies revealed that BBO reached the best results over all the case studies decisively.
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