Quick Combinatorial Artificial Bee Colony -qCABC- Optimization Algorithm for TSP

Combinatorial Artificial Bee Colony Algorithm (CABC) is a new version of Artificial Bee Colony (ABC) to solve combinatorial type optimization problems and quick Artificial Bee Colony (qABC) algorithm is an improved version of ABC in which the onlooker bees behavior is modeled in more detailed way. Studies showed that qABC algorithm improves the convergence performance of standard ABC on numerical optimization. In this paper, to see the performance of this new modeling way of onlookers' behavior on combinatorial optimization, we apply the qABC idea to CABC and name this new algorithm as quick CABC (qCABC). qCABC is tested on Traveling Salesman Problem and simulation results show that qCABC algorithm improves the convergence and final performance of CABC

Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks

Self-generated fuzzy systems design using artificial bee colony optimization

In this paper, artificial bee colony (ABC) optimization based methodology is proposed for automatically extracting Takagi-Sugeno (TS) fuzzy systems with enhanced performance from data. The design procedure aims to find the structures and the parameters of the TS fuzzy systems simultaneously without knowing the rule number as a priori. In the proposed method, a fuzzy system is encoded into a food source with appropriate string representation so that the TS model is entirely specified. The encoded premise and consequent parameters of the fuzzy model evolve together through artificial bee colony optimization strategy simulating the global foraging behavior of honey bee swarm so that good solutions can be achieved. Simulations on benchmark modeling and tracking control problems are performed and compared with other existing methods. The experimental results indicate that the proposed ABC optimization based fuzzy systems design algorithms can successfully find accurate fuzzy models with appropriate number of rules, Moreover, the proposed approach outperforms the compared methods and can provide considerable improvements in tackling complex modeling and tracking control problems. (C) 2014 Elsevier Inc. All rights reserved

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance

Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip

Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks