Introduction to Fruit Fly Optimization Algorithm - FFOA

Fruit fly optimization algorithm (FFOA) was originally proposed in Pan (2011). It is based on the food foraging behaviour of fruit fly.

   (フルーツフライ最適化アルゴリズムの紹介 -  FFOA)

   The harsh environmental factors are powerful forces in the process of natural selection. Those organisms ablest to adapt and survive will produce stronger progeny, whereas the others expire prior to passing on their genetic traits or create less adapted descendants. Similarly, these conditions can be modeled in an artificial environment through evolutionary computation algorithms by populating an artificial world with pseudo-organisms and giving it the ultimate goal [23, 38]. The idea of genetic (evolutionary computation) algorithm was pioneered from studies of cellular automata conducted by John Holland et al. [24] at the University of Michigan. It utilizes genetics as the basis of its problem-solving model, and with this model we can through 'evolution' teach the organisms to achieve the stated goal. It has numerable applications to the problems we currently face, especially if the goal set is a formulation of the problem we confront in our society.

   Particularly in the fields of science, engineering, and economics, these stochastic optimization algorithms have been increasingly used to solve many optimization problems due to their incredible flexibility. Amongst these algorithms include genetic algorithm (GA) [2,4,5,8,9,21, 24,34,37], simulated annealing (SA) [26,31], biology- based optimization algorithms — ant colony optimization algorithm (ACO) [13], particle swarm optimization algorithm (PSO) [6,11,14,15,23,30,35,40,46,49,50,53], artificial bee colony algorithm (ABCA) [3,22,27,28,29], artificial fish school algorithm (AFSA) [26,33], shuffled frog leaping algorithm (SFLA) [16,48,58], bacterial foraging optimization(BFO) [36,45], and cat swarm optimization (CSO) [10,57] etc. However, these stochastic algorithms share the common disadvantage of complicated computational processes that make it difficult to understand for those just beginning to learn to use these algorithms.

   The Fruit Fly Optimization Algorithm is an easy evolution algorithm for maximizing global optimization based on the food searching behavior of the fruit fly swarm. Fruit flies have better senses and perceptions than other species, particularly its olfactory and visual senses. The individual fruit fly samples the differing scents that are present in its surroundings, then uses its sensitive vision to fly in the general direction of the target. It transmits information to the rest of the swarm and direct the ‘flocking location' of the swarm closer to the location of the food. Through ‘iterative evolution,” the fly swarm comes closer and closer to the food source, until the target is reached.

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Jing Si Aphorism:

Use wisdom to contemplate the meaning of life.

Use the resolve to organize the time you are given. 

Fruit fly optimization algorithm (FFOA) was originally proposed in Pan (2011). It is based on the food foraging behaviour of fruit fly.

   Soochow University EMA

Categories: FFOA, Fruit Fly Optimization Algorithm