An Enhanced Educational Competition Optimizer Integrating Multiple Mechanisms for Global Optimization Problems

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, LSHADE-SPACMA, EOSMA, GLSRIME, EPSCA, and ESLPSO. Across the entire experimental battery, EECO consistently occupied the first place in the Friedman hierarchy: it attained average ranks of 2.138 in 10-D, 1.438 in 30-D, 1.207 in 50-D, and 1.345 in 100-D CEC-2017 benchmarks, together with 1.722 on the nine real-world engineering problems, corroborating its superior and dimension-scalable performance. The Wilcoxon rank sum test confirms the statistical significance of these improvements. With its remarkable convergence accuracy and reliable stability, EECO emerges as a promising variant of the ECO algorithm.

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