Elite leader dwarf mongoose optimization algorithm

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Dwarf Mongoose Optimization Algorithm (DMOA) is a recent metaheuristic known for solving complex optimization problems.
• A limitation of DMOA is its roulette wheel selection, which can lead to uneven swarm evolution and rapid diversity loss.

Purpose of the Study:

• To propose a novel two-stage structured elite leader dwarf mongoose optimization algorithm (EL-DMOA).
• To enhance the performance and overcome the limitations of the original DMOA.

Main Methods:

• EL-DMOA introduces a two-stage approach: a leader stage and a follower stage.
• The leader stage uses artificial fitness for leader selection and a differential operator for leader evolution.
• The follower stage utilizes elite leaders to guide the swarm and incorporates crossover operations to maintain diversity.

Main Results:

• Experiments on the CEC2017 test suite and real-world problems demonstrate EL-DMOA's superior performance.
• EL-DMOA outperformed FIPS, DE/rand/1, and four other recent metaheuristics.
• The differential operator effectively improved the quality of swarm leaders.

Conclusions:

• The proposed two-stage structure of EL-DMOA promotes even and efficient swarm evolution.
• EL-DMOA effectively addresses the limitations of DMOA, enhancing its optimization capabilities.
• The algorithm shows significant potential for complex optimization tasks.