Power System Optimisation using Ant Lion Optimisation Technique

Vikhe, Akshay Sunil; Kalage, Amol A

doi:10.1109/iceca.2019.8822093

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Update the boundary of a random walk using equation (10)(11) Create a random walk and normalize it using equation (5-7) Update the positions of ants using equation (14) Update elite if an antlion becomes fitter than the elite Calculate the values of the fitness of all ants. Replace an antlion with its corresponding ant using equation (13) Update the positions of antlions using equation ( 23), (24) and (25) N Y Calculate the fitness of the antlions, and find the best ant lion as an elite…”

Section: Set Parametersmentioning

confidence: 99%

“…Researchers found that ALO surmounts other famous techniques like genetic algorithm (GA) and particle swarm optimization for various engineering problems [11]. At present, ALO algorithm is successfully applied to multiobjective transformer design optimization [12], power system optimization [13], WSN data gathering [14], route planning for unmanned aerial vehicle [15], etc. However, similar to other intelligent algorithms, the ALO algorithm also has problems such as slow convergence and easy to fall into local optima, and its optimization ability still needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

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Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks

Chen

Yang

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.

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