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Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective...
This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this...
This paper presents a metaheuristic optimization-based approach to mobile robot path planning problem. A comparative study between trajectory-based metaheuristic optimization and population-based metaheuristic optimization is conducted. Breadth-first deterministic search is used to find the optimal solution (ground truth) that is compared to the paths generated by tabu search, simulated annealing...
The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm...
Data compression is very important today and it will be even more important in the future. Textual data use only limited alphabet - total number of used symbols (letters, numbers, diacritics, dots, spaces, etc.). In most languages, letters are joined into syllables and words. All three approaches are useful in text compression, but none of them is the best for any file. This paper describes a variant...
This paper presents binary particle swarm optimization (BPSO) for finding an optimum conflict-free transmission schedule for a broadcast radio network. This is known as Broadcast Scheduling Problem (BSP) and shown as an NP-complete problem in earlier studies. Because of this NP-complete nature, earlier studies used genetic algorithms, mean field annealing, neural networks, factor graph and sum product...
General compression algorithms were designed for usage characters as basic symbols. Later, algorithm which used words were developed. The problem is that there is no clear line which defines if is better to use characters or words. In this paper, we developed optimization algorithm based on simulated annealing that selects only several words from all possible words and combine them with character...
Quadratic assignment problem (QAP) is a hard and classical combinatorial optimization problem. Simulated annealing algorithm has been successfully applied to solve QAP. However, the search of simulated annealing algorithm might usually get stuck with local optima due to the low acceptable moves, particularly when the barrier is high and the temperature is low. In this paper, we propose a tabu-based...
Node localization in wireless sensor networks (WSNs) is important for applications such as military surveillance, environmental monitoring, robotics, and many others. The sensor motes used in this type of application present low-power and low-cost profile. Hence, they require methods that compute their positions using indirect information such as Received Signal Strength Indicator (RSSI). This work...
In this paper we have used two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson–Kessel clustering (GKC) along with local information for unsupervised change detection in multitemporal remote sensing images. In conventional FCM and GKC no spatio-contextual information is taken into account and thus the result is not so much robust to small changes. Since the pixels are highly correlated...
This study looks at the system availability optimization problem under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related...
This paper brings out the studies of generation scheduling problem in an electrical power system. This paper presents some general reviews of research and developments in the field of unit commitment based on published articles and web-sites. Here, it is set about to perform a comprehensive survey of research work made in the domain of Unit Commitment using various techniques. This may be a helpful...
In this study, a nonlinear forecasting model is proposed in order to obtain accurate prediction results and ameliorate forecasting performances. In the model, the genetic algorithm (GA) is coupled with simulated annealing (SA) algorithms to evolve a back-propagation neural network (BPNN) algorithm, called GASANN. The new model's performance is compared with three individual forecasting models, namely...
Due to the critical blood shortages in South Africa and around the world, the assignment of blood can be considered an important real world optimization problem. This paper presents a mathematical model that facilitates good management and assignment of red blood cell units in order to minimize the quantity of imported units from outside the system. The model makes use of the Multiple Knapsack Algorithm,...
The availability of different flavor of processor architecture coupled with computer codes of various nature poses a discreet challenge to the programmers in forms of code optimization. Programmers need to contemplate on optimization during pre and post implementation to take advantage of the hardware given for a specific nature of the code. To compliment this requirement, the evolution of compiler...
The authors examine optimization of the antenna array beamformer bank for a technique to apply coherent signal subspace processing in the beam-space of a bank of true time delay beamformers. This technique offers computational expense advantages over element-space methods, Mean Square Error (MSE) performance similar to wideband coherent subspace techniques and is frequency invariant. This article...
In this paper, we deal with a preventive maintenance (PM) scheduling and spare parts problems for a rolling stock system. We determine the optimal PM interval and the optimal number of spare parts for components in the rolling stock system to minimize the system life cycle cost during satisfying the system target availability. The system availability and system life cycle cost are estimated by simulation...
recently, genetic algorithm and their evolutionary algorithms are widely used on automatic test data generation, but they have many problems such as local optimum, premature convergence and being difficult to find global optimum. This paper proposes a new algorithm: SA-QGA (simulated annealing - quantum generate algorithm), and introduces the Boltzmann mechanism of SA into B_QGA (QGA based on Boltzmann...
The paper presents power transformer design, using genetic algorithm (GA) and simulated annealing (SA) by minimizing total active cost, keeping in view the constraints imposed by international standards and power transformer specifications. The design results using conventional method (CM) have been compared with those obtained by applying GA and SA techniques and it is quite evident that the dimensions...
Optical packet switching (OPS), a promising technology for small, high-capacity networks such as metro or regional networks, leverages optical transparency to decrease the number of interfaces to be deployed and the energy consumption of a network when compared with an opaque technology. Based on those principles, ring-based OPS techniques such as POADM (Packet Optical Add-Drop Multiplexers) were...
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