No Free Lunch (NFL) theorem has logically proved that there is no meta-heuristic algorithm best suited for solving all optimization problems. We optimize the symmetrical bell shaped function using Whale Optimization. It is a swarm based meta-heuristic algorithm inspired by the hunting behavior of humpback whales. The exploitation phase involves both encircling and spiral hunting. Spiral hunting simulates the bubble-net attacking mechanism of whales. Balance between both exploration and exploitation phases help in converging towards a better solution. The potential field data satisfies Laplace’s equation which on a routine basis can be interpreted through analytical signal and can be approximated by a symmetric bell shape function. It is a non linear equation and depends upon the amplitude factor which is related to the physical property, the horizontal location, depth and the shape of the causative source. We inverted a synthetic magnetic data over two thin dykes with opposite polarity, field magnetic data from Barraute in Canada and field SP data over a Copper deposit in India. The synthetic results are in agreement with the assumed model while the field example agrees with drill hole data. These examples were also inverted through other two swarm based algorithms i.e., Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). The performance of WOA is comparable or better than that of GWO and PSO.

Presentation Date: Wednesday, October 17, 2018

Start Time: 1:50:00 PM

Location: Poster Station 4

Presentation Type: Poster