.png)
Ackley Function
Number of variables: n=2
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Find the minimal value of the Ackley function by my modified 3D-FOA:
Final Result:
Soochow University EMA
Saturday, August 30, 2014
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(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.
|
Soochow University EMA
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