evolutionary algorithms

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 ɛ-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.

In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This hybrid algorithm finds difficult minority instances; then, their misclassification cost will be calculated using the proposed cost measure. Also, to improve classification performance, the lateral tuning of membership functions (in data base) is employed by means of a genetic algorithm. The performance of the proposed method is compared with some cost-sensitive classification approaches taken from the literature. Experiments are performed over 37 imbalanced datasets from KEEL dataset repository; the classification results are evaluated using the Area Under the Curve (AUC) as a performance measure. Results reveal that our hybrid cost-sensitive fuzzy rule-based classifier outperforms other methods in terms of classification accuracy.

Directional overcurrent relays (DOCRs) are widely used to protect power systems. For optimal coordination of DOCRs, several techniques have been proposed to solve this problem. A common way of optimal coordination of DOCRs is using evolutionary algorithms such as genetic algorithm (GA). In this paper, a novel strategy for DOCRs coordination is proposed. In the proposed strategy, a new objective function (OF) is introduced. The proposed objective function can removed mis coordination between paired relays and can result in better coordination. Proposed OF is applied to 6 bus and 30-bus sample networks.

Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic algorithms have been employed increasingly during recent years. In this study, a new algorithm, namely Seeker Evolutionary Algorithm (SEA), is introduced for solving continuous mathematical problems, which is based on a group seeking logic. In this logic, the seekingregion and the seekers located inside are divided into several sections and they seek in that special area. In order to assess the performance of this algorithm, from the available samples in papers, the most visited algorithms have been employed. The obtained results show the advantage of the proposed SEA incomparison to these algorithms. At the end, a mathematical problem is designed, which is unlike the structure of meta-heuristic algorithms. All the prominent algorithms are applied to solve this problem, and none of them is able to solve.