Path planning for an underwater vehicle ran be formulated as a multiobjective optimization problem, which can be solved by modern heuristic techniques. For assessment of a trajectory, three crucial criteria are used: ...
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ISBN:
(纸本)8371435150
Path planning for an underwater vehicle ran be formulated as a multiobjective optimization problem, which can be solved by modern heuristic techniques. For assessment of a trajectory, three crucial criteria are used: a total length of a path, a smoothness of a trajectory, and a measure of safety. A multiobjective evolutionary algorithm for finding Pareto-optimal solutions is proposed. Then, the underwater vehicle and navigation in three dimensions is considered. Some results of numerical simulations are presented.
Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw ve...
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ISBN:
(纸本)9798400701207
Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.
evolutionary algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if...
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ISBN:
(纸本)9783030581114;9783030581121
evolutionary algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMaxa describing the number of matching bits with a fixed target alpha is an element of{0, 1}(n).
The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The incr...
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ISBN:
(纸本)9781728183923
The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.
evolutionary algorithms (EAs) are mainly considered for modelling and solving practical complex and NP-hard problems in large-scale search spaces. The aim of this paper is to apply some well-known intelligent optimiza...
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ISBN:
(纸本)9781467322270
evolutionary algorithms (EAs) are mainly considered for modelling and solving practical complex and NP-hard problems in large-scale search spaces. The aim of this paper is to apply some well-known intelligent optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for solving routing and wavelength assignment (RWA) problem in optical networks which is also known to be an NP-hard problem. The performance of proposed optimization algorithms is compared for convergence speed and solution accuracy. The NSFNET network is considered as test-bench topology and randomly generated connection requests are introduced into network demand matrix. Simulation results demonstrate that the convergence speed of ABC algorithm is much better than other two algorithms to reach near-optimum solution in acceptable processing time. Furthermore, the PSO algorithm has better performance than GA in term of convergence speed.
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.
This paper deals with the problem of the distribution of images over the nodes of a cluster-based architecture in order to minimize the response time of interactive queries that trigger a dynamic image retrieval proce...
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ISBN:
(纸本)1892512459
This paper deals with the problem of the distribution of images over the nodes of a cluster-based architecture in order to minimize the response time of interactive queries that trigger a dynamic image retrieval process. The problem is formalized and a novel representation of solutions is introduced. Since the general problem is NP-complete a lot of effort has been put into developing algorithmic approaches based on greedy-algorithms and local-search procedures. This paper introduces an evolutionary algorithm that improves the performance of all approaches.
evolutionary algorithms have been widely used for a range of stochastic optimization problems. In most studies, the goal is to optimize the expected quality of the solution. Motivated by real-world problems where cons...
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ISBN:
(纸本)9781450361118
evolutionary algorithms have been widely used for a range of stochastic optimization problems. In most studies, the goal is to optimize the expected quality of the solution. Motivated by real-world problems where constraint violations have extremely disruptive effects, we consider a variant of the knapsack problem where the profit is maximized under the constraint that the knapsack capacity bound is violated with a small probability of at most a. This problem is known as chance-constrained knapsack problem and chance-constrained optimization problems have so far gained little attention in the evolutionary computation literature. We show how to use popular deviation inequalities such as Chebyshev's inequality and Chernoff bounds as part of the solution evaluation when tackling these problems by evolutionary algorithms and compare the effectiveness of our algorithms on a wide range of chance-constrained knapsack instances.
This paper is devoted to the development and study of evolutionary algorithms for solving multiobjective problems of high-speed digital electronic PCB design. The paper examines the criteria and constraints of the PCB...
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ISBN:
(纸本)9781467369619
This paper is devoted to the development and study of evolutionary algorithms for solving multiobjective problems of high-speed digital electronic PCB design. The paper examines the criteria and constraints of the PCB design problems, and the research results are described.
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