Abstract
<p class="Abstract"><span lang="EN-GB">Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. </span><span lang="EN-AU">Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA) and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.</span></p>
Highlights
Swarm Intelligence is a collective behavior of decentralized, self-organized natural or artificial systems
There are a number of simple agents which follow simple and fixed rules that determine their possible behavior during interaction among themselves and the surroundings
In order to test the performance of the modified version of Firefly algorithm (MoFA), we compare its results with the results obtained using other commonly applied heuristic algorithms
Summary
Swarm Intelligence is a collective behavior of decentralized, self-organized natural or artificial systems. Optimization problems based on swarm intelligence are known as meta-heuristic algorithms [9] which have features like self-organization, no central control, derivative free and easy to implement. These features lead to an emergent behavior that overcome the main limitations of conventional methods and can be conveniently applied to various optimization problems. These metaheuristic algorithms have two major components namely exploitation and exploration. Later a comprehensive comparative study on the performance of the proposed algorithm (MoFA) with the standard Firefly algorithm (SFA) and other metaheuristic algorithms for optimizing a very large set of numerical functions is presented
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