Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for non-convex global optimization

Abstract

This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling within ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to approach the global optimum, simulating the evolution of an ecological system toward its optimal state of stability and balance. Moreover, a parameter sensitivity analysis is conducted on 23 classic optimization functions to determine a suitable default configuration for ECO. Furthermore, 30 competitive metaheuristic algorithms are selected to form an algorithm pool, and comprehensive experiments are conducted on the IEEE CEC-2014 and CEC-2017 test suites. Among these, five top-performing algorithms, namely ARO, CFOA, CSA, WSO, and INFO, are chosen for an in-depth comparison with the ECO on the IEEE CEC-2020 test suite, verifying the ECO's exceptional optimization performance. Finally, in order to validate the practical applicability of ECO in complex real-world engineering problems, five state-of-the-art algorithms, including FDB-AGDE, FDB-SFS, LRFDB-COA, L-SHADE, and NSM-SFS are selected for comparative experiments on five engineering problems from the CEC-2020-RW test suite, demonstrating that ECO achieves competitive performance against advanced engineering-oriented algorithms. The ECO project page is available at https://jxxsteven7.github.io/ECO-Optimizer/.

34 pages, 16 figures