This paper presents the results of an experimental investigation on solving graph coloring problems with evolutionary algorithms (EAs). After testing different algorithm variants we conclude that the best option is an...
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This paper presents the results of an experimental investigation on solving graph coloring problems with evolutionary algorithms (EAs). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping Genetic Algorithm (GGA) on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping (GA) and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.
Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement ...
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Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement noise, modelling errors, etc). This paper addresses the aforementioned research challenges by proposing evolutionary algorithms-based self-tuning for first-order Takagi-Sugeno-Kang-type fuzzy logic controller (FLC). We consider three major state-of-the-art optimisation algorithms, namely, Genetic Algorithm, Particle Swarm Optimisation, and Artificial Bee Colony to facilitate automatic tuning. The effectiveness of the proposed control schemes is tested and compared under several different flight conditions, such as, constant, varying step and sine functions. The results show that the ABC-FLC outperforms the GA-FLC and PSO-FLC in terms of minimising the settling time in the absence of overshoots. (C) 2019 Elsevier B.V. All rights reserved.
Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a co...
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Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a combinatorial optimization problem. Several evolutionary algorithms (EAs) have been proposed to deal with this complex problem. Encouraging results have been achieved by using such approaches. However, the running time may be very high or even prohibitive in applications of EAs to large-scale networks. This limitation may be critical for problems requiring online solutions. The performance obtained by EAs for network reconfiguration is drastically affected by the adopted computational tree representation. Inadequate representations may drastically reduce the algorithm performance. Thus, the employed representation for chromosome encoding and the corresponding operators are very important for the performance achieved. An efficient data structure for tree representation may significantly increase the performance of evolutionary-based approaches for network reconfiguration problems. The present paper proposes a tree encoding and two genetic operators to improve the EA performance for network reconfiguration problems. The corresponding EA approach was applied to reconfigure large-scale systems. The performance achieved suggests that the proposed methodology can provide an efficient-alternative for reconfiguration problems.
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.
This study aims to investigate the deployment of a proposed search field division method (SFDM) within evolutionary algorithms (EAs) to enhance the capability of searching for the global optima in nonlinear problems. ...
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This study aims to investigate the deployment of a proposed search field division method (SFDM) within evolutionary algorithms (EAs) to enhance the capability of searching for the global optima in nonlinear problems. The proposed technique is benchmarked against the following eight widely-used single-modal, multi-modal, and unimodal benchmark functions: Sphere, Rosenbrock, Rastringin, Griewank, Ackley, Fletcher, Quartic, and Schwefel functions, and the outcome is compared to their standard EAs counterparts to validate the effectiveness of the deployed approach in EAs. In the proposed method, we apply three low, medium, high field divisions (1, 2, and 5) dimensions on nine different EAs simultaneously with two different scenarios, 10 and 100 variables, to reach the optimal solution. Then for the validity of our proposed SFDM technique, we examined the exploration-exploitation search space rates and diversity behavior. The results of the implementation of SFDM on eight benchmark test functions show that the consideration of dimensions using SFDM for EAs improves the outcomes of all nine tested EAs. In our proposed method, we find better compatibility with the integration of SFDM in the Particle Swarm Optimization Algorithm concerning searching for the optimum solution relative to the other EAs.
The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is sti...
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The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimize the benefits of adding CAVs to existing multi-modal transport systems. Using a real-world scenario from the Leeds Metropolitan Area as a case study, we demonstrate an effective way of combining macro-level mobility simulations based on open data with global optimisation techniques to discover realistic optimal deployment strategies for CAVs. The macro-level mobility simulations are used to assess the quality of a potential multi-route CAV service by quantifying geographic accessibility improvements using an extended version of Dijkstra's algorithm on an abstract multi-modal transport network. The optimisations were carried out using several popular population-based optimisation algorithms that were combined with several routing strategies aimed at constructing the best routes by ordering stops in a realistic sequence.
Previous investigations indicated that a flat-walled, multi-layered anechoic lining system, with an overall thickness slightly less than a quarter of a wavelength, could be used to achieve a required cut-off frequency...
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Previous investigations indicated that a flat-walled, multi-layered anechoic lining system, with an overall thickness slightly less than a quarter of a wavelength, could be used to achieve a required cut-off frequency. However, the work proved to be tedious and time consuming because of the numerous trial-and-error measurements involved. On the other hand, the successful application of a method of calculating the overall acoustic impedance of multi-layered absorbing systems has indicated that the design of multi-layered absorbing systems can be carried out on a desktop computer. In the present work, a MATLAB genetic and evolutionary algorithm toolbox is implemented as the optimiser to aid and speed up the design process. The optimisation results indicate that a three-layered lining system can achieve results comparable with quality wedge-type anechoic linings with overall thickness slightly less than a sixth of a wavelength at the 100 Hz cut-off frequency. (C) 2004 Elsevier Ltd. All rights reserved.
evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may ...
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evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may depend drastically on these parameters. Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best static parameters in EAs on discrete problems. However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size lambda in the non-elitist (1,lambda) EA. It is known that the (1,lambda) EA has a sharp threshold with respect to the choice of lambda where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear whether parameter control mechanisms are able to find and maintain suitable values of lambda. For OneMax we show that the answer crucially depends on the success rate s (i. e. a one-(s + 1)-th success rule). We prove that, if the success rate is appropriately small, the self-adjusting (1, lambda) EA optimises OneMax in O(n) expected generations and O(n log n) expected evaluations, the best possible runtime for any unary unbiased black-box algorithm. A small success rate is crucial: we also show that if the success rate is too large, the algorithm has an exponential runtime on OneMax and other functions with similar characteristics.
Memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hur...
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Memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hurdle problem, a landscape class of tunable difficulty with a "big valley structure", a characteristic feature of many hard combinatorial optimisation problems. A parameter called hurdle width describes the length of fitness valleys that need to be overcome. We show that the expected runtime of plain evolutionary algorithms like the (1+1) EA increases steeply with the hurdle width, yielding superpolynomial times to find the optimum, whereas a simple memetic algorithm, (1+1) MA, only needs polynomial expected time. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, it becomes easier for memetic algorithms. We further give the first rigorous proof that crossover can decrease the expected runtime in memetic algorithms. A (2+1) MA using mutation, crossover and local search outperforms any other combination of these operators. Our results demonstrate the power of memetic algorithms for problems with big valley structures and the benefits of hybridising multiple search operators. (C) 2020 Elsevier B.V. All rights reserved.
This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the no...
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This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of multiple models for optimization problems, and shows that it is possible to mix fast low-fidelity models for exploration and expensive high-fidelity models for exploitation. Finally, a new class of multi-objective optimizers mixing HGAs and Nash Game Theory is defined. These methods are tested for solving design optimization problems in aerodynamics. A parallel version of this approach running a cluster of PCs demonstrate the convergence speed up on an inverse nozzle problem and a high-lift problem for a multiple element airfoil. (C) 2002 Published by Elsevier Science B.V.
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