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...
详细信息
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...
详细信息
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...
详细信息
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.
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...
详细信息
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...
详细信息
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.
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...
详细信息
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.
The crude oil preheating process in refineries is required to be scheduled in a way to minimize the processing cost involved with it, subject to the satisfaction of various process related constraints. The process for...
详细信息
ISBN:
(数字)9783319916415
ISBN:
(纸本)9783319916415;9783319916408
The crude oil preheating process in refineries is required to be scheduled in a way to minimize the processing cost involved with it, subject to the satisfaction of various process related constraints. The process forms a mixed-integer optimization problem as the scheduling of the processing units involves binary variables, while the discharges from the running units are real valued. The two parts of such problems are usually handled by two different algorithms, where the optimum scheduling obtained by one algorithm is fed to another algorithm for optimizing its discharge process. In the present work, formulating the crude oil preheating process under the effect of linear fouling as a mixed-integer nonlinear programming (MINLP) model, three binary-real coded evolutionary algorithms (EAs) are investigated in order to demonstrate that a single EA can successfully tackle its both binary and real parts. Further, the statistical analysis of the performances of the EAs are also presented through their application to a benchmark instance of the problem.
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...
详细信息
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.
Component selection is a crucial problem in Component Based Software Engineering. Component Based Software Engineering (CBSE) is concerned with the assembly of preexisting software components that leads to a software ...
详细信息
ISBN:
(纸本)9780769532998
Component selection is a crucial problem in Component Based Software Engineering. Component Based Software Engineering (CBSE) is concerned with the assembly of preexisting software components that leads to a software System that responds to client-specific requirements. We are approaching the component selection involving dependencies between components (requirements). We formulate the problem as multiobjective. The approach used is an evolutionary computation technique. Various representations were used with various applied methods to deal with the multiobjectives.
暂无评论