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Elite leader Dwarf Mongoose Optimization Algorithm
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Abstract
To improve the performance of original dwarf mongoose optimization algorithm, this study proposes an elite leader dwarf mongoose optimization algorithm (EL-DMOA). EL-DMOA adopts two stage structure. The leader stage employs differential operator to improve the selected leaders. The artificial fitness is introduced for selecting the swarm leaders. If one individual’s fitness ceasing to improve, the artificial fitness method will imposes additional punishment to the relevant individual, thereby the newly founded solution is encouraged to lead the swarm. In the follower stage, each individual learns from the leaders. The crossover operation is employed to enhance swarm diversity. The experiments on CEC2017 test suite and real-life application problems show that EL-DMOA performs better than FIPS, DE/rand/1 and four recently meta-heuristics.
Title: Elite leader Dwarf Mongoose Optimization Algorithm
Description:
Abstract
To improve the performance of original dwarf mongoose optimization algorithm, this study proposes an elite leader dwarf mongoose optimization algorithm (EL-DMOA).
EL-DMOA adopts two stage structure.
The leader stage employs differential operator to improve the selected leaders.
The artificial fitness is introduced for selecting the swarm leaders.
If one individual’s fitness ceasing to improve, the artificial fitness method will imposes additional punishment to the relevant individual, thereby the newly founded solution is encouraged to lead the swarm.
In the follower stage, each individual learns from the leaders.
The crossover operation is employed to enhance swarm diversity.
The experiments on CEC2017 test suite and real-life application problems show that EL-DMOA performs better than FIPS, DE/rand/1 and four recently meta-heuristics.
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