Abstract
AbstractIn this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms.
Highlights
Optimization problems have become a major field of interest for researchers in different propensities, and created an immense bunch of movement widely used in engineering applications
The modified Black Hole algorithm has been proposed for solving optimization problems
A new approach is employed to overcome the probable drawbacks caused by the trapping effect, where trapping is suppressed and particles close to the black hole are swallowed with a new swallowing range if they exceed the minimum distance to the black hole
Summary
Optimization problems have become a major field of interest for researchers in different propensities, and created an immense bunch of movement widely used in engineering applications. Nature and bio inspired algorithms seem superior to their classic counterparts and have found specific places between the newfangled algorithms [1]. The effectiveness of these algorithms had been proven in many fields such as industry, hardware design, urban design, routing, image processing, project scheduling, Controller design, robots path planning, data clustering and etc. The BH algorithm and its modified versions have been used to solve optimization and engineering problems [5,6,7,8,9,10,11,12,13,14,15,16,17,18]
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