Discussing the article: "Central Force Optimization (CFO) algorithm"
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Check out the new article: Central Force Optimization (CFO) algorithm.
The force of attraction is calculated according to rules similar to Newton's law of universal gravitation. It depends on the difference in "weight" between the probes (the difference in the quality of solutions) and the distance between them. A probe with a high fitness function value strongly attracts nearby probes with low values, but has little effect on distant samples. Under the influence of these forces, each probe is accelerated and begins to move. Small, "light" probes rush towards the "heavier" ones, as if balls were rolling down the slopes of hills to the top. With each step of the algorithm, the probes recalculate the forces of attraction and adjust their movement. If a probe attempts to move beyond the established boundaries of the search area, a reflection mechanism is triggered – imagine that there is a wall at the edge of the area, from which the probe bounces back into the allowed area.
Over time, the probes begin to collect around high points in the landscape. Most of them are concentrated around the most promising areas, and with each iteration they determine the position of the peaks more accurately. Ideally, if you give the algorithm enough time, all the probes should converge around the global maximum — the highest point in the entire landscape.
The peculiarity of CFO is that it is essentially a deterministic algorithm - if you run it twice with the same initial distribution of probes, it will give the same result. This distinguishes it from many other metaheuristic algorithms that rely on randomness.
Author: Andrey Dik