A hybrid multi‐objective tour route optimization algorithm based on particle swarm optimization and artificial bee colony optimization

2020 ◽  
Vol 36 (3) ◽  
pp. 884-909 ◽  
Author(s):  
Romit Beed ◽  
Arindam Roy ◽  
Sunita Sarkar ◽  
Durba Bhattacharya
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Route Optimization ◽  
Swarm Optimization ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals Optimal PMU Placement using Binary Particle Swarm and Artificial Bee Colony with Channel Limitations and Redundancy.

2019 ◽  
Vol 8 (3) ◽  
pp. 3881-3886
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Measurement Unit ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Phasor Measurement Unit (PMU) being expensive and to be placed optimally, a meta-heuristic approach of Binary particle swarm optimization (BPSO) and Binary artificial bee colony optimization (BABC) is made for the optimal allocation of PMU in a power system. The PMU locations resulted are served by basic system conditions like network configuration, critical generators, and loads. The pattern of locations on including Zero-Injection Buess (ZIB) is also discussed. The redundancy in case of PMU loss is coined so as to obtain a complete observability of the power system. the channel limitations of device is also taken into consideration for better results in real-time systems. Optimal PMU locations for IEEE 30-bus and 14-bus systems with channel limits are compared with all above considerations. The number of PMU locations is reduced as channel limits increases. The simulated PMU locations are decreased with improved observability by Binary Artificial Bee Colony Optimization as compared to Binary Particle Swarm Optimization.

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

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