Elephant nose fish searches its food such as larvae by active electrolocation. It discharges electric pulse through its electric organ in tail and detects the object by analyzing the geometrical property of projected electrical image on it. The capacitance value found out from that electric image helps the fish to reach near the food source. Shark also uses passive electrolocation for the same purpose. It can target its prey by sensing the electrical wave generated due to the muscle twitching of small living beings in water. Both the above physiological phenomena, concerning the active and passive electrolocation of fish, has been mathematically developed as nature-inspired meta-heuristic technique named fish electrolocation optimization (FEO). A comparative study based on benchmark functions has been done amongst real coded genetic algorithm, accelerated particle swarm optimization, particle swarm optimization, harmony search and the proposed algorithm. Furthermore, comparative study has been done with simulated annealing and differential evolution on eggcrate function. The proposed technique has also been implemented on real-world optimization problem related to cost-based reliability enhancement in radial distribution system. It can be said by comparing percentage of success, mean number of function evaluation and standard deviation that FEO algorithm works better than other mentioned meta-heuristic techniques.
['Vivekananda Haldar', 'Niladri Chakraborty']
A novel evolutionary technique based on electrolocation principle of elephant nose fish and shark: fish electrolocation optimization