An Enhanced Educational Competition Optimizer Integrating Multiple Mechanisms for Global Optimization Problems
Na Li, Zi Miao, Sha Zhou, Haoxiang Zhou, Meng Wang, Zhenzhong Liu

TL;DR
This paper introduces EECO, an improved optimization algorithm that outperforms existing methods in solving complex global optimization problems.
Contribution
EECO introduces three novel mechanisms to enhance diversity, exploitation, and exploration–exploitation balance in optimization.
Findings
EECO achieved higher solution accuracy and smaller standard deviations than eight recent algorithms on CEC-2017 benchmarks.
EECO consistently ranked first in the Friedman hierarchy across multiple dimensions and real-world engineering problems.
Statistical tests confirm the improvements in EECO's performance are significant.
Abstract
The Educational Competition Optimizer (ECO) formulates search as a three-stage didactic process—primary, secondary and tertiary learning—but the original framework suffers from scarce information exchange, sluggish late-stage convergence and an unstable exploration–exploitation ratio. We present EECO, which introduces three synergistic mechanisms: a regenerative population strategy that uses the covariance matrix of elite solutions to maintain diversity, a Powell mechanism that accelerates exploitation within promising regions, and a trend-driven update that adaptively balances exploration and exploitation. EECO was evaluated on the 29 benchmark functions of CEC-2017 and nine real-world constrained engineering problems. Results show that EECO delivers higher solution accuracy and markedly smaller standard deviations than eight recent algorithms, including EDECO, ISGTOA, APSM-jSO,…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Vehicle Routing Optimization Methods
