Abstract
The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.
Highlights
Inspired by various biological systems in nature, many scholars have proposed effective methods that simulate natural evolution to solve complex optimization problems
To improve the performance of the firefly algorithm (FA), this paper proposes an improved FA based on selfadaptive inertia weight logarithmic and dynamic step-size adjust factor (LWFA)
Fireflies have a unique lighting mechanism and behavior, and the light they emit can only be perceived by other individual fireflies within a certain range for two reasons: the light intensity I and the distance from the light source r are in inverse proportion, and the light can be absorbed by air
Summary
Inspired by various biological systems in nature, many scholars have proposed effective methods that simulate natural evolution to solve complex optimization problems. Sharma introduced the inertia weight into the FA; this strategy can overcome the tendency of falling into local optima and can achieve a slow convergence for optimization problems [16]. An FA based on parameter adjustment is better than PSO in solving dynamic optimization problems. Adiland and other scholars improved the self-adaptation of the search mechanism and parameters of individual fireflies and embedded chaotic mapping to solve a mechanical design optimization problem [19]. To improve the performance of the FA, this paper proposes an improved FA based on selfadaptive inertia weight logarithmic and dynamic step-size adjust factor (LWFA). Self-adaptive logarithmic inertial weight is introduced in the updating formula This strategy can effectively balance the exploration and exploitation capabilities and improve the convergence speed of the algorithm.
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