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This paper proposes a new kind of constraint handling method for optimization. In the proposed method, a transformation of constraint functions to be another objective function will be discussed in details. With this technique we can transform the constraint problem to be unconstraint multiobjective optimization and use the multi-objective optimization methods to find feasible solutions. The performance...
To move the sample points towards the optimality according to an attraction-repulsion mechanism, an improved heuristic is proposed. Firstly, the constrained optimization problem is transformed into an unconstrained optimization problem by external point method. Secondly, the formula of the particle charge is re-defined to decrease the amount of calculation and improve the efficiency. Then the formula...
Rule-based classifiers have been successfully applied in data mining applications. In this Paper, we have proposed a novel rule generator classifier called CORER (Colonial competitive Rule-based classifier) to improve the accuracy of data classification. The proposed classifier works based on CCA (Colonial Competitive Algorithm), a recently-developed evolutionary optimization algorithm. In order to...
In this paper, we consider the problem of minimizing the resources used for network coding (MRUNC) while achieving the desired throughput in a multicast system. The problem of minimizing the number of network coding links is NP-hard. In this paper we propose a low-complexity Estimation of Distribution Algorithm (EDA) for MRUNC. Our EDA is applicable to the network with and without cycles. The numerical...
The principle of antenna array distribution of two-dimensional synthetic aperture microwave radiometer is presented. The optimization technique based on differential evolution algorithm (DE) is presented for the design of antenna array. The object function is designed by minimizing redundant array and maximizing the distance between spatial frequency coverage samples, the stochastic array distributed...
In this study, we present the Big Bang-Big Crunch (BB-BC) method to solve the post-enrolment course timetabling problem. This method is derived from one of the evolution of the universe theories in physics and astronomy. The BB-BC theory involves two phases (Big Bang and Big Crunch). The Big Bang phase feeds the Big Crunch phase with many inputs and the Big Crunch phase is the shrinking destiny of...
In China, More than 458 cities and more than 50% of the population are distributed in natural disaster-prone areas. It is important to establish a complete post-disaster reconstruction and rehabilitation system to respond to sudden natural disasters. In order to get post-disaster restoration items scheduling, we invited experts to give the fuzzy preference relation coefficients on the restoration...
Evolutionary algorithms are an important branch of soft computing, being able to provide approximate solutions to problems in a reasonable amount of time. The underlying principle can be realized in an almost unlimited number of ways. This paper presents four main variants of evolutionary algorithms, and a method of running them in a topology consisting of multiple populations. The resources given...
Traditional multicast technology faces a serious state scalability problem when there are large numbers of concurrent groups in the network. As a new approach to solve this scalability problem, aggregated multicast forces multiple multicast groups to share a common distribution tree. This can be defined as a minimum grouping problem and is proved to be an NPC problem. An ant colony optimization algorithm...
Various techniques of fitness landscape analysis for the determination of optimisation problem hardness for evolutionary algorithms are proposed in the literature. However, a few implementations of these techniques and their application in practice are described nowadays. In this paper fitness landscapes of benchmark fitness functions are analysed. Statistical and information measures of fitness landscapes...
We describe a method for accurately estimating the topology of sensor networks from time-series data collected from infrared proximity sensors. Our method is a hybrid combining two different methodologies: ant colony optimization (ACO), which is an evolutionary computation algorithm; and an adjacency score, which is a novel statistical measure based on heuristic knowledge. We show that, using actual...
Designing gas turbines is a very complex task. It is not a linear procedure but an iterative one, composed by several phases. In the initial phase, the general geometric characteristics and estimate efficiency of the turbine are determined. This phase is known as the meanline design, and it is very important because it determines the starting point for more complex analysis. In this work we use a...
The composition of Web services is an interesting option for the creation of complex applications with a wide range of features. The inherent scalability of the Internet leads to a potentially large number of services that meet a particular feature. Moreover, these services are associated with different quality indicators, sometimes conflicting. Hence, it is evident the plurality of solutions for...
Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian...
This paper proposes an application of Evolutionary Algorithms (EAs) such as Differential Evolution (DE), Evolutionary Programming (EP), Real coded Genetic Algorithm (RGA), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Particle Swarm Optimization (PSO) to Reactive Power Planning (RPP) problem. RPP is a non-smooth and non-differentiable optimization problem for a multiobjective function...
In order to exploit and preserve the diversity of immune optimization algorithm when solving high dimensional global optimization problems, a novel immune optimization algorithm based lifespan (LIO) model is proposed. LIO incorporates a lifespan model, local and global search procedure to improve the overall performance in solving global optimization instance. Particularly, a novel performance evaluation...
The optimization of a supply chain is a very hard problem for classical optimization methods. Some evolutionary algorithms have been used to deal with supply chain problem recently. In this paper, Migration Differential Evolution (MDE) is proposed by imitating nomadic migration for this supply chain problem, and an ensemble method based on different Differential Evolutions (DEs) has been presented,...
Intelligent watermarking (IW) allows adapting embedding parameter for each image and set of attacks using evolutionary computing. However, IW is not practical in real-world applications because of the computational cost of (evolutionary computing) algorithms that must be applied to optimize the parameters for each document image. It is however possible to formulate IW as a dynamic optimization problem...
Evolutionary Algorithms can be inefficient in optimizing problems in which fitness evaluation of candidate solutions is computationally expensive. In this paper, single and multi-objective evolutionary methods assisted by meta-models are proposed and analyzed. Meta-models are used to identify promising regions of search space in order to save evaluations of objective-functions. The meta-models are...
An evolutionary approach is presented to solve the topology optimization of the structures with tension-only or compression-only materials. To find their optimal topologies by traditional methods is very hard for the sake of the material in the structures should be considered as nonlinear and an isotropic one in structural analysis process. To avoid such difficulties, the reference-interval with material-replacement...
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