A novel evolutionary technique based on electrolocation principle of elephant nose fish and shark: fish electrolocation optimization


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.

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Fig. 1
Fig. 2


diff :

Difference between maximum and minimum limit of solution variable

longrange :

A set of discrete values in long range

shortrange :

A set of discrete values in short range

\(p1_l ,p2_l ,p3_l \) :

Constant terms for longrange formulation

\(p1_s ,p2_s ,p3_s \) :

Constant terms for shortrange formulation

vshortrange :

A set of discrete values in very short range

ikj :

Index terms

slopevs :

Electrical image slope, short distance interval value

xnew, \(x^{\min }\) and \(x^{\max }\) :

Calculated solution value after evolution, minimum and maximum limit of solution variable ‘x

\({elec}^{{pulse}}\) :

Value of electric pulse for generation of new electrical wave

capucapl :

Capacitor upper limit, capacitor lower limit

capintcaphover :

Initial capacitor value, capacitor value when the conceptual electro-fish is hovering and searching

randfloorfixrandpermrandn and length :

Standard MATLAB\(^{\textregistered }\) 7.0 library functions

\({rand}^i, {rand}_j^i \) :

Random value for ith individual amongst population,random value for ith individual and jth variable

\({prob}^{{div}}, {prob}^{{sel}}\) :

Probability of divergence, probability of selection

\({prob}^{{rng}}\) :

Probability of range

\(x_{{best}}^t , x_{{worst}}^t \) :

Best found variable value at tth iteration, worst found variable value at tth iteration

ch1 and ch2:

Minimum and maximum value of objective function for the first iteration

g1, g2:

Constant terms for distance calculation

\({cap}^{{run}}\) :

Running capacitor value

toggle :

Toggle switch or changeover switch

\(\sigma ( {x_i })\) :

Symbol for standard deviation function

\({slope}^{{const}}\) :

A constant value for \({elec}^{{pulse}}\) generation

Sm1 and m2:

Array of random index terms concerning the length of longrangeshortrange and vshortrange

nh1 and h2:

Selected random values from s, m1 and m2

c2 and c3:

Values concerning shortrange and vshortrange


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The authors would like to give thanks to Department of Science and Technology, Government of India, New Delhi, INSPIRE Fellowship for their financial support to pursue the research work satisfactorily. The authors also like to give thanks to Dr. Kamal Krishna Mandal for his valuable suggestion. Special thanks to the teachers and staffs of Power Engineering Department, Jadavpur University, for their co-operation.

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Correspondence to Vivekananda Haldar.

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Haldar, V., Chakraborty, N. A novel evolutionary technique based on electrolocation principle of elephant nose fish and shark: fish electrolocation optimization. Soft Comput 21, 3827–3848 (2017). https://doi.org/10.1007/s00500-016-2033-1

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  • Capacitance
  • Electrolocation
  • Optimization
  • Electric image