Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

改进蚁群算法的无线传感器网络路径优化

研究无线传感器网络路径优化问题,针对无线传感器网络(WSN)路径优化问题,在分析了遗传算法和蚁群算法各自优缺点的基础上,通过把蚁群算法作为WSN路径优化的主框架,采用遗传算的选择、交叉和变异算子提高蚁群算法搜索速度,提出一种改进蚁群算法的WSN路径优化方法。仿真结果表明,改进蚁群算法有效地克服了基本蚁群算法的缺陷,提高了WSN路径优化效率和成功率,减少了能理消耗,有效延长了网络生存时间。     

基于改进蚁群算法的维修拆卸序列规划

维修拆卸序列规划是整个维修性设计的重要内容。为了能够以较高的效率求解出产品中零件的拆卸方案,依据产品的基本信息和零件之间的约束关系,建立拆卸Petri网可达图,将拆卸序列规划问题转化为对Petri网可达图最优路径的搜索和寻优问题。同时利用蚁群优化算法对组合优化具有高强适应性的特征,改进基本蚁群算法,对可达图模型进行路径寻优,得到最优或次优的拆卸序列。最后通过实例验证了该方法的有效性。     

基于群智能混合算法的物流配送路径研究

针对物流车辆路径优化问题,考虑到基本蚁群算法有收敛速度慢、易陷入局部最优的缺点,采用了一种双种群蚁群算法,在蚁群的基础上引入差分进化(DE)和粒子群算法(PSO)。通过在PSOAS种群和DEAS种群之间建立一种信息交流机制,使信息能够在两个种群中传递,以免某一方因错误的信息判断而陷入局部最优点。通过matlab仿真实验测试,表明该群智能混合算法可以较好地解决TSP的问题。     

Analysis of Reusability using ‘Marginal Reuse Rate’

While reuse is an effective lifecycle option in terms of reduction of environmental loads and value of reutilization, reuse has inherent difficulties. Our naive question is why component reuse of home appliances seems impossible while that of photocopiers succeeded. This paper clarifies an essential factor for successful reuse; that is, the balance between supply and demand of reusables, and proposes an index named ‘marginal reuse rate,’ which indicates upper limit of reusability. By using this index, reusability of several products is analyzed. The marginal reuse rate indicates that design of lifecycle, in addition to product design, is indispensable for successful reuse.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation

Metaheuristic optimization algorithms have become a popular choice for solving complex problems which are otherwise difficult to solve by traditional methods. However, these methods have the problem of the parameter adaptation and many researchers have proposed modifications using fuzzy logic to solve this problem and obtain better results than the original methods. In this study a comprehensive review is made of the optimization techniques in which fuzzy logic is used to dynamically adapt some important parameters in these methods. In this paper, the survey mainly covers the optimization methods of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), which in the last years have been used with fuzzy logic to improve the performance of the optimization methods.