The approximation error of an evolutionary algorithm is the fitness difference between the optimal solution and a solution found by the algorithm. In this paper, an initial error analysis has been made to evolutionary...
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ISBN:
(纸本)9781450349390
The approximation error of an evolutionary algorithm is the fitness difference between the optimal solution and a solution found by the algorithm. In this paper, an initial error analysis has been made to evolutionary algorithms for discrete optimization. First, the order of convergence and asymptotic error constant are defined. Then it is proven that for any EA, under particular initialization, its order of convergence is 1 and its asymptotic error constant equals to the spectral radius of the transition probability sub-matrix;if its transition probability sub-matrix is primitive or upper triangular with unique diagonal entries, then under random initialization, its order of convergence is 1 and its asymptotic error constant equals to the spectral radius of the transition probability sub-matrix. Our study reveals that evolutionary algorithms converge linearly to the optimal solution and the spectral radius of the transition probability sub-matrix is the main factor in affecting the approximation error.
In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge incre...
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In the last years, several real-world problems that require to optimise an increasing number of variables have appeared. This type of optimisation, called large-scale global optimisation, is hard due to the huge increase of the domain search due to the dimensionality. Large-scale global optimisation is a research area getting more attention in the last years, thus many algorithms, mainly evolutionary algorithms, have been specially designed to tackle it. In this paper, we give a brief introduction of several of them and their techniques, remarking techniques based on grouping of variables and memetic algorithms, because they are two promising approaches. Also, we have reviewed the winners of the different competitions in the area, to give a snapshot of the algorithms that have obtained the best results in this area. Finally, several interesting trends in the research area have been pointed out, and some future trends and challenges have been suggested.
Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals...
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ISBN:
(纸本)9781509020478
Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals that needs to be accomplished. This paper presents the implementation of three evolutionary algorithms (Genetic algorithms, Particle Swarm Optimization and Ant Colony Optimization) for the JSSP. The tests are made considered a set of classical benchmarks for the proposed problem and the obtained results are subject to comparison.
The resource constrained project scheduling problem(RCPSP) has received wide attention in the last 20 years with a number of evolutionary algorithms being proposed. Most of these algorithms can produce optimal or near...
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ISBN:
(纸本)9781509025978
The resource constrained project scheduling problem(RCPSP) has received wide attention in the last 20 years with a number of evolutionary algorithms being proposed. Most of these algorithms can produce optimal or near optimal solutions in less than a second. However, a close investigation of the literature will reveal a number of questionable benchmarking practices. In this paper I highlight some of these issues together with possible future research directions which are mainly centred around the use of hyper-heuristics.
In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, opti...
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In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances. Published by AIP Publishing.
The task planning of satellite-ground time synchronization (SGTSTP) is a complex many-objective ground station scheduling problem. In this paper, we first provide a mathematical formulation of SGTSTP. To solve this pr...
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Six modern and promising evolutionary algorithms are described: genetic algorithm, differential evolution method, variational genetic algorithm, particle swarm optimization algorithm, bat-inspired method and firefly a...
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Six modern and promising evolutionary algorithms are described: genetic algorithm, differential evolution method, variational genetic algorithm, particle swarm optimization algorithm, bat-inspired method and firefly algorithm. For all algorithms brief description and main steps of receiving solution are given. In the experimental part all algorithms are compared by the effectiveness of solving the parametric optimization problem for PID controllers.
The optimal sizing in water distribution networks (WDN) is of great interest because it allows the selection of alternative economical solutions that ensure design requirements at nodes (demands and pressure) and at l...
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The optimal sizing in water distribution networks (WDN) is of great interest because it allows the selection of alternative economical solutions that ensure design requirements at nodes (demands and pressure) and at lines (velocities). Among all the available design methodologies, this work analyzes those based on evolutionary algorithms (EAs). EAs are a combination of deterministic and random approaches, and the performance of the algorithm depends on the searching process. Each EA features specific parameters, and a proper calibration helps to reduce the randomness factor and improves the effectiveness of the search for minima. More specifically, the only common parameter to all techniques is the initial size of the random population ( P ). It is well known that population size should be large enough to guarantee the diversity of solutions and must grow with the number of decision variables. However, the larger the population size, the slower the convergence process. This work attempts to determine the population size that yields better solutions in less time. In order to get that, the work applies a method based on the concept of efficiency ( E ) of an algorithm. This efficiency relates the quality of the obtained solution with the computational effort that every EA requires to find the final design solution. This ratio E also represents an objective indicator to compare the performance of different algorithms applied to WDN optimization. The proposed methodology is applied to the pipe-sizing problem of three medium-sized benchmark networks, such as Hanoi, New York Tunnel and GoYang networks. Thus, from the currently available algorithms, this work includes evolutionary methodologies based on a Pseudo-Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Harmony Search (HS). First, the different algorithm parameters for each network are calibrated. The values used for every EA are those that have been calculated in previous works. Secondly, specific para
In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users'...
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In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users' mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration. (C) 2015 Elsevier B.V. All rights reserved.
Due to the increasing amount of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries...
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Due to the increasing amount of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries. Besides, this paper aims to contribute humanitarian relief chains under uncertainty. In this paper, we address a humanitarian logistics network design problem including local distribution centers (LDCs) and multiple central warehouses (CWs) and develop a scenario-based stochastic programming (SBSP) approach. Also, the uncertainty associated with demand and supply information as well as the availability of the transportation network's routes level after an earthquake are considered by employing stochastic optimization. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly minimize the maximum travel time between each pair of facility and the demand point of the items. Additionally, a data set derived from a real disaster case study in the Iran area, and to solve the proposed model a exact method called epsilon-constraint in low dimension along with some well-known evolutionary algorithms are applied. Also, to achieve good performance, the parameters of these algorithms are tuned by using Taguchi method. In addition, the proposed algorithms are compared via four multi-objective metrics and statistically method. Based on the results, it was shown that: NSGA-II shows better performances in terms of SNS and CPU time, meanwhile, for NPS and MID, MRGA has better performances. Finally, some comments for future researches are suggested.
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