Abstract
Firefly Algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches used for solving optimization problems. The conventional FA use randomization factor during generation of solution search space and fireflies position changing, which results in imbalanced relationship between exploration and exploitation. This imbalanced relationship causes in incapability of FA to find the most optimum values at termination stage. In the proposed model, this issue has been resolved by incorporating PS at the termination stage of standard FA. The optimized values obtained from the FA are set as the initial starting points for the PS algorithm and the values are further optimized by PS to get the most optimal values or at least better values than the values obtained by conventional FA during its maximum number of iterations. The performance of the newly developed FA-PS model has been tested on eight minimization functions and six maximization functions by considering various performance evaluation parameters. The results obtained have been compared with other optimization algorithms namely genetic algorithm (GA), standard FA, artificial bee colony (ABC), ant colony optimization (ACO), differential equations (DE), bat algorithm (BA), grey wolf optimization (GWO), Self-Adaptive Step Firefly Algorithm (SASFA), and FA-Cross algorithm in terms of convergence rate and various numerical performance evaluation parameters. A significant improvement has been observed in the solution quality by embedding PS in the standard FA at the termination stage. The result behind this improvement is the better exploration and exploitation of the solution search space at this stage.
Highlights
Optimization is the process of finding the solution of a problem with the most cost-effective or highest possible achievable performance using the resources in hand by minimizing the undesired factors and maximizing the desired ones
As it can be observed in all the minimization functions, there is no observable change in the convergence rate of these functions after that specified iterations for all the optimization algorithms considered in our experimentation except the standard Firefly Algorithm (FA) in which there are still some fluctuations after these iterations but these fluctuations take a smooth convergence rate after few iterations
Similar to other standard optimization techniques, FA is suffered from imbalanced relationship between the exploration and exploitation capability of the solution search space that leads to degraded solution quality resulting in not getting the most optimal solution
Summary
Optimization is the process of finding the solution of a problem with the most cost-effective or highest possible achievable performance using the resources in hand by minimizing the undesired factors and maximizing the desired ones. Making sophisticated nets by worms and termites and search of food by ants and bees in a systematic approach is the result of their well-managed coordinated and collective behavior All these swarms utilize two phenomena namely exploration and exploitation to make their coordinated system more powerful in achieving their desired goals. The swarm of individuals shows a collective behavior based on a self-organized and decentralized coordination system for achieving different goals like reproduction, foraging, food search and other day to day activities. The rest of the paper is organized as follows: Section II shows related work; Section III presents the proposed solution; Section IV shows experimental results; Section V presents results discussion while Section VI presents the conclusion and future work
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