Introduction and comparison of firefly algorithm and metaheuristic solving algorithm

The firefly algorithm is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies and is designed to solve continuous optimization problems.

Principle of Firefly Algorithm

In the firefly algorithm, the objective function is related to the light intensity at the tail of the firefly. In terms of optimization, the attraction and movement of fireflies can inspire algorithms, which can be followed to obtain optimal solutions.

In the firefly algorithm, fireflies refer to randomly generated feasible solutions. They are assigned a light intensity proportional to their functional value based on their performance in the objective function. For minimization problems, the solution with the smallest function value will be assigned the highest light intensity. Once the light intensities of the solution are distributed, each firefly will follow the firefly with the higher light intensity. The brightest firefly will perform a local search by randomly moving around it.

The algorithm mimics how fireflies interact using flash lights. Assume that all fireflies are opposite-sex attracted, which means that any firefly can attract all other fireflies. The attractiveness of a firefly is directly proportional to its brightness, which depends on the objective function. Brighter fireflies attract other fireflies. Furthermore, according to the inverse square law, brightness gradually decreases with distance.

Firefly Algorithm and Metaheuristic Solving Algorithm

Fireflies communicate using flickering properties, and there are approximately 2,000 unique flash patterns. They produce brief, patterned flashes of light.

This flashing pattern of communication is used to attract mates and warn predators. The right partner will communicate by imitating the same pattern or responding in a specific pattern. Therefore, a firefly's flash causes a reaction in nearby fireflies.

Natural selection and survival of the fittest are the core ideas of early metaheuristic algorithms. Due to the complexity of algorithm modeling, the implementation of deterministic solution methods is challenging, thus promoting the development of metaheuristic solution algorithms.

Metaheuristic algorithms are approximate solutions to optimization problems that use the properties of randomness to iterate and improve the quality of the solution from a set of randomly generated feasible solutions.

Although metaheuristic algorithms cannot guarantee optimality, they can be tested to give reasonable and acceptable solutions.

Additionally, metaheuristic algorithms have the advantage of being immune to problem behavior, which makes them useful in many application scenarios.

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