Tandra Pal

SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers

This paper presents a self-organized genetic algorithm-based rule generation (SOGARG) method for fuzzy logic controllers. It is a three-stage hierarchical scheme that does not require any expert knowledge and input-output data. The first stage selects rules required to control the system in the vicinity of the set point. The second stage extends this to the entire input space, giving a rulebase that can bring the system to its set point from almost all initial states. The third stage refines the rulebase and reduces the number of rules. The first two stages use the same fitness function whose aim is only to acquire the controllability, but the last stage uses a different one, which attempts to optimize both the settling time and number of rules. The effectiveness of SOGARG is demonstrated using an inverted pendulum and the truck reversing.

Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015

The proceedings of the 4thInternationalConferenceon 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.

On rule pruning using fuzzy neural networks

Shann and Fu (SF) proposed a fuzzy neural network (FNN) for rule pruning in a fuzzy controller. In this paper we first analyze the FNN of SF and discuss some of its limitations. SF attempted to eliminate redundant rules interpreting some of the connection weights as certainty factors of rules. In their strategy the connection weights are unrestricted in sign and hence their interpretation as certainty factors introduces some inconsistencies into the scheme. We propose a modification of this FNN, which eliminates these inconsistencies. Moreover, we also propose a pruning scheme which, unlike the scheme of SF, always produces a compatible rule set. Superiority of the modified FNN is established using the inverted pendulum problem.

ASMiGA: An Archive-Based Steady-State Micro Genetic Algorithm

We propose a new archive-based steady-state micro genetic algorithm (ASMiGA). In this context, a new archive maintenance strategy is proposed, which maintains a set of nondominated solutions in the archive unless the archive size falls below a minimum allowable size. It makes the archive size adaptive and dynamic. We have proposed a new environmental selection strategy and a new mating selection strategy. The environmental selection strategy reduces the exploration in less probable objective spaces. The mating selection increases searching in more probable search regions by enhancing the exploitation of existing solutions. A new crossover strategy DE-3 is proposed here. ASMiGA is compared with five well-known multiobjective optimization algorithms of different types-generational evolutionary algorithms (SPEA2 and NSGA-II), archive-based hybrid scatter search, decomposition-based evolutionary approach, and archive-based micro genetic algorithm. For comparison purposes, four performance measures (HV, GD, IGD, and GS) are used on 33 test problems, of which seven problems are constrained. The proposed algorithm outperforms the other five algorithms.

Secure Receipt-Free Sealed-Bid Electronic Auction

The auction scheme that provides receipt-freeness, prevents the bidders from bid-rigging by the coercers. Bid-rigging is a dangerous attack in electronic auction. This happen if the bidder gets a receipt of his bidding price, which proves his bidding prices, from the auction protocol. The coercers used to force the bidders to disclose their receipts and hence bidders lose the secrecy of their bidding prices. This paper presents a protocol for a receipt-free, sealed-bid auction. The scheme ensures the receipt-freeness, secrecy of the bid, secrecy of the bidder and public verifiability.

A solid transportation model with product blending and parameters as rough variables

In this paper, we formulate a practical solid transportation problem with product blending which is a common issue in many operational and planning models in the chemical, petroleum, gasoline and process industries. In the problem formulation, we consider that raw materials from different sources with different quality (or purity) levels are to be transported to some destinations so that the materials received at each destination can be blended together into the final product to meet minimum quality requirement of that destination. The parameters such as transportation costs, availabilities, demands are considered as rough variables in designing the model. We construct a rough chance-constrained programming (RCCP) model for the problem with rough parameters based on trust measure. This RCCP model is then transformed into deterministic form to solve the problem. Numerical example is presented to illustrate the problem model and solution strategy. The results are obtained using the standard optimization solver LINGO.

Multiple attribute group decision making using interval-valued intuitionistic fuzzy soft matrix

A noticeable progress has been found in decision making problems since the introduction of soft set theory by Molodtsov in 1999. It is found that classical soft sets are not suitable to deal with imprecise parameters whereas fuzzy soft sets (FSS) are proved to be useful. Use of intuitionistic fuzzy soft sets (IFSS) is more effective in environment, where arguments are presented using membership and non-membership values. In this paper we propose an algorithmic approach for multiple attribute group decision making problems using interval-valued intuitionistic fuzzy soft matrix (IVIFSM). IVIFSM is the matrix representation of interval-valued intuitionistic fuzzy soft set (IVIFSS), where IVIFSS is a natural combination of interval-valued intuitionistic fuzzy set and soft set theory. Firstly, we propose the concept of IVIFSM. Then an algorithm is developed to find out the desired alternative(s) based on product interval-valued intuitionistic fuzzy soft matrix, combined choice matrix, and score values of the set of alternatives. Finally, a practical example has been demonstrated to show the effectiveness of the proposed algorithm.

The Fuzzy Robust Graph Coloring Problem

Fuzzy graph model can represent a complex, imprecise and uncertain problem, where classical graph model may fail. In this paper, we propose a fuzzy graph model to represent the examination scheduling problem of a university and introduce a genetic algorithm based method to find the robust solution of the scheduling problem that remains feasible and optimal or close to optimal for all scenarios of the input data. Fuzzy graph coloring method is used to compute the minimum number of days to schedule the examination. But problem arises if after the examination schedule is published, some students choose new courses in such a way that it makes the schedule invalid. We call this problem as fuzzy robust coloring problem (FRCP). We find the expression for robustness and based on its value, robust solution of the examination schedule is obtained. The concept of fuzzy probability of fuzzy event is used in the expression of robustness, which in turn, is used for fitness evaluation in genetic algorithm. Each chromosome in the genetic algorithm, used for FRCP, represents a coloring function. The validity of the coloring function is checked keeping the number of colors fixed. Fuzzy graphs with different number of nodes are used to show the effectiveness of the proposed method.

A new archive based steady state genetic algorithm

In this paper we have proposed a new archive based steady state multi-objective genetic algorithm, which performs well, especially in higher dimensional space. An improved archive maintenance strategy has been introduced in this algorithm which is adaptive as well as dynamic in size. The archive maintenance strategy tries to maintain only the set of nondominated solutions in the archive. However, it maintains a minimum size of population when the nondominated solutions are not sufficient to fill the population. In this algorithm we have proposed a new environmental selection and a new mating selection. The mating selection reduces the exploration in less probable search region enhancing the exploitation of existing solutions. A new crossover operator DE-3 has also been proposed in this article. The proposed algorithm has been compared with three other existing multi-objective optimization algorithms NSGA-II, SPEA2 and AbYSS. Our algorithm outperforms the other three algorithms for its better diversity and convergence to true Pareto optimal front.

Image Clustering Based on Different Length Particle Swarm Optimization (DPSO)

Partitioning image pixels into several homogeneous regions is treated as the problem of clustering the pixels in the image matrix. This paper proposes an image clustering algorithm based on different length particle swarm optimization algorithm. Three evaluation criteria are used for the computation of the fitness of the particles of PSO based clustering algorithm. A novel Euclidean distance function is proposed based on the spatial and coordinate level distances of two image pixels towards measuring the similarity/dissimilarity. Different length particles are encoded in the PSO to minimize the user interaction with the program hence the execution time. PSO with different length particles automatically finds the number of cluster centers in the intensity space.