Sudip Kumar Sahana

An Improved Modular Hybrid Ant Colony Approach for Solving Traveling Salesman Problem

primary aim is to design a framework to solve the well known traveling salesman problem(TSP) using combined approach of Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Several solutions exists for the above problem using ACO or GA and even using a hybrid approach of ACO and GA. Our framework gives the optimal solution for the above problem by using the modular hybrid approach of ACO and GA along with heuristic approaches.We have incorporated GA, RemoveSharp and LocalOpt heuristic approaches in ACO module, hence each iteration calls the GA and heuristics within ACO module which results in a higher amount of pheromone deposited in the optimal path for global pheromone update. As a result the convergence is quicker and solution is optimal. KeywordsSalesman Problem(TSP),Ant Colony Optimization (ACO), Genetic Algorithm (G.A), Heuristics, Optimization, Pheromone.

High Performance Ant Colony Optimizer (HPACO) for Travelling Salesman Problem (TSP)

Travelling Salesman Problem (TSP) is a classical combinatorial optimization problem. This problem is NP-hard in nature and is well suited for evaluation of unconventional algorithmic approaches based on natural computation. Ant Colony Optimization (ACO) technique is one of the popular unconventional optimization technique to solve this problem. In this paper, we propose High Performance Ant Colony Optimizer (HPACO) which modifies conventional ACO. The result of implementation shows that our proposed technique has a better performance than the conventional ACO.

Nested hybrid evolutionary model for traffic signal optimization

A noble Nested Hybrid Evolutionary Model is presented to reduce the wait time of vehicles at traffic signals and improve the mobility within the road network. In effect, it contributes towards achieving green environment and reducing the fuel consumption. The proposed model is based on Bi-level Stackelberg Game in which the upper layer is “traffic signals” which is optimized using evolutionary computational techniques (ACO, GA and a Hybrid of ACO and GA) and the lower layer is “stochastic user equilibrium” for which road network is designed using Petri Net (PN) respectively. A comparative analysis has been carried out and it was found that nested hybrid model outperforms ACO and GA.

Hybrid Synchronous Discrete Distance Time Model for Traffic Signal Optimization

This paper proposes a novel solution to the traffic signal optimization problem by reducing the wait time of individual vehicle users at intersections within the urban transportation system. Optimized signal timings, not only reduce the wait time of vehicle users but also improve the mobility within the system. In effect, it also reduces the ever increasing emissions and fuel consumption. A novel synchronous discrete distance-time model is proposed to frame the problem on the basis of 2-layer Stackelberg game. Thereafter, the upper layer optimization is solved using evolutionary computation techniques (ACO, GA and a Hybrid of ACO and GA). A comparative analysis done over the aforementioned techniques indicates that the hybrid algorithm exhibits better performance for the proposed model.

Advances in Computational Intelligence

Includes a special section containing interdisciplinary applications of computational intelligence Includes keynote lectures delivered at the conference Covers work in emerging areas in the field of computational intelligence

A Comprehensive Survey on Computational Grid Resource Management

The paper attempts to give a complete report on different methods of resource management in grid computing. The extensive usage of internet applications and its popularity has driven an ongoing demand of increased bandwidth and high computational power. Resource management is a challenging task in grid environment as the workload is high and quick responses to the user’s query are necessary in real time. The aim of this paper is to collect various algorithms used in grid scheduling at one place so that it will help the new researchers in their course of work. So, proper resource scheduling becomes extremely important not only because resources are heterogeneous in nature, but their availability also changes with time in a grid environment. This paper will cover most of the scheduling algorithms that can be useful to any researcher and will provide substantial help to his research work.

An Efficient Swarm-Based Multicast Routing Technique—Review

Multicast routing is emerging as a popular communication format for networks where a sender sends the same data packet to multiple nodes in the network simultaneously. To support this, it is important to construct a multicast tree having minimal cost for every communication session. But, because of dynamic and unpredictable environment of the network, multicast routing turns into a combinatorial issue to locate a best path connecting a source node and destination node having minimum distance, delay and congestion. To overcome this, various multicast conventions have been proposed. As of late, swarm and evolutionary techniques such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) and genetic algorithm (GA) have been adopted by the researchers for multicast routing. Out of these, ACO and GA are most popular. This paper shows an important review of existing multicast routing techniques along with their advantages and limitations.

A Survey on Multiprocessor Scheduling Using Evolutionary Technique

In this paper, various conventional approaches are studied for the task scheduling, precedence–resource constrained, load balancing, and multiprocessor scheduling problems. In parallel machines the sequence of dependent execution setup time for the minimization of makespan in scheduling problems and prepared a concise review. Multiprocessor scheduling is an NP-hard problem, whereas scheduling algorithm schedules the tasks which may or may not be dependent on each other. There are several traditional approaches existing for processor scheduling such as modified critical path (MCP), dominant sequence clustering (DSC), and priority-based multichromosome (PMC). While using these approaches, we achieve partial solutions in less than the minimum computing time. In this paper, an innovative multiprocessor scheduling technique that is inspired by evolutionary techniques has been embodied.

A Novel Multimedia Encryption and Decryption Technique Using Binary Tree Traversal

In recent years, there is vast improvement in the field of telecommunication and digital world. While upsurge of the traffic will increase the security issue for any individuals. While transferring multimedia contents through the communication channels, the primary concern is the security of the data. To cope with this problem, there are several traditional and novel encryption algorithms for protecting the multimedia contents. The multimedia encryption is well defined in three categories: (i) Complete encryption (ii) Selective encryption, and (iii) Combined compression—Encryption. In this paper, the primary focus is on four block ciphers based complete encryption technique which uses a bit level substitution and permutation components to achieve high security, speed, and less computational complexity for real-time data processing. A novel algorithm is applied using a binary tree traversal for multi-bit word parallelism using substitution and a two-dimensional array to perform a nonlinear diffusion process. The result is shown in term of encryption time, memory usage, and CPU utilization. A comparison has been performed with conventional algorithms likes AES, RC5, and RC6 to prove the superiority of our proposed algorithm.

A Survey on Grid Schedulers

A grid is an infrastructure to meet the ongoing demands of science and engineering (Foster et al. in Int J High Perform Comput Appl 13(3):200–222, 2001) [1]. In the midst of the 1980s and the 1990s, researchers observed that parallel computing and distributed computing was not only sufficient for solving the biggest challenges of engineering problems. They needed some mechanism which could utilize the power of distributed as well as parallel computing. Grid computing (Foster and Kesselmen in The grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, pp 1–593, 1999; Jacob et al. in Introduction to grid computing, 1st edn., 2005) [2, 3] was the solution to their problem. But working in a grid environment is not really easy since the grid users are increasing and services are becoming commercial, so it is desirable to free the users from the load of job handling. The grid scheduler or resource broker performs the task of job handling such as resource management and fulfilling user requirements. In this chapter, an anatomy of grid schedulers has been discussed which discusses intrinsic properties of different grid schedulers.