Interval Type 2 Fuzzy Set in Fuzzy Shortest Path Problem

The shortest path problem (SPP) is one of the most important combinatorial optimization problems in graph theory due to its various applications. The uncertainty existing in the real world problems makes it difficult to determine the arc lengths exactly. The fuzzy set is one of the popular tools to represent and handle uncertainty in information due to incompleteness or inexactness. In most cases, the SPP in fuzzy graph, called the fuzzy shortest path problem (FSPP) uses type-1 fuzzy set (T1FS) as arc length. Uncertainty in the evaluation of membership degrees due to inexactness of human perception is not considered in T1FS. An interval type-2 fuzzy set (IT2FS) is able to tackle this uncertainty. In this paper, we use IT2FSs to represent the arc lengths of a fuzzy graph for FSPP. We call this problem an interval type-2 fuzzy shortest path problem (IT2FSPP). We describe the utility of IT2FSs as arc lengths and its application in different real world shortest path problems. Here, we propose an algorithm for IT2FSPP. In the proposed algorithm, we incorporate the uncertainty in Dijkstra’s algorithm for SPP using IT2FS as arc length. The path algebra corresponding to the proposed algorithm and the generalized algorithm based on the path algebra are also presented here. Numerical examples are used to illustrate the effectiveness of the proposed approach

Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system

This article proposes an efficient meta-heuristic approach, namely, oppositional grey wolf optimization (OGWO) algorithm for resolving the optimal operating strategy of economic load dispatch (ELD) problem. The proposed algorithm combines two basic concepts. Firstly, the hunting behavior and social hierarchy of grey wolves are used to search optimal solutions and secondly, oppositional concept is integrated with the grey wolf optimization (GWO) algorithm to accelerate the convergence rate of the conventional GWO algorithm. To show the performance of the proposed algorithm, it is applied on small, medium and large scale test systems for solving ELD problems of 13-unit, 40-unit and 160-unit systems. Comparative studies are carried out to scrutinize the efficiency of the proposed OGWO approach over the conventional GWO and other approaches available in the literature. The simulation results clearly suggest that the proposed OGWO approach is capable of finding better solutions in terms of computational time and fuel cost than the other techniques. Keywords: Economic load dispatch, Evolutionary algorithm, Grey wolf optimization, Oppositional based learning, Power syste

4th International Conference on Frontiers in Intelligent Computing : Theory and Applications

The proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications 2015 (FICTA 2015) serves as the knowledge centre not only for scientists and researchers in the field of intelligent computing but also for students of post-graduate level in various engineering disciplines. The book covers a comprehensive overview of the theory, methods, applications and tools of Intelligent Computing. Researchers are now working in interdisciplinary areas and the proceedings of FICTA 2015 plays a major role to accumulate those significant works in one arena. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like Nature Inspired Algorithms, Fuzzy Systems, Data Mining, Signal Processing, Image processing, Text Processing, Wireless Sensor Networks, Network Security and Cellular Automata. 

Opposition-based krill herd algorithm applied to economic load dispatch problem

Economic load dispatch (ELD) is the process of allocating the committed units such that the constraints imposed are satisfied and the production cost is minimized. This paper presents a novel and heuristic algorithm for solving complex ELD problem, by employing a comparatively new method named krill herd algorithm (OKHA). KHA is nature-inspired metaheuristics which mimics the herding behaviour of ocean krill individuals. In this article, KHA is combined with opposition based learning (OBL) to improve the convergence speed and accuracy of the basic KHA algorithm. The proposed approach is found to provide optimal results while working with several operational constraints in ELD and valve point loading. The effectiveness of the proposed method is examined and validated by carrying out numerical tests on five different standard systems. Comparing the numerical results with other well established methods affirms the proficiency and robustness of proposed algorithm over other existing methods. Keywords: Economic load dispatch, Valve point loading, Opposition based learning, Krill herd algorith