EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Educational Competition Optimizer (ECO) is a human-based metaheuristic with good performance but limited exploration and exploitation capabilities.
• Basic ECO suffers from premature convergence and reduced population diversity in complex optimization scenarios.

Purpose of the Study:

• To propose an enhanced Educational Competition Optimizer (EDECO) to address the limitations of the basic ECO algorithm.
• To improve global exploration, population quality, convergence speed, and the balance between exploitation and exploration.

Main Methods:

• Incorporation of an Estimation of Distribution Algorithm (EDA) to enhance global exploration and population quality.
• Replacement of some best individuals using a dynamic fitness distance balancing strategy for adaptive convergence and exploration-exploitation balance.

Main Results:

• EDECO demonstrated significant improvements over the basic ECO and four other advanced algorithms on 29 CEC 2017 benchmark functions.
• EDECO showed significant superiority in solving 10 real-world engineering constrained optimization problems.

Conclusions:

• The proposed EDECO algorithm effectively overcomes the limitations of the basic ECO.
• EDECO offers a powerful and effective approach for tackling complex optimization challenges in both benchmark and engineering applications.