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

2025-08-28

AI Summary

This paper addresses global optimization problems—including unconstrained, constrained, and complex real-world engineering applications—by proposing a novel swarm intelligence algorithm, the Ecological Cycling Optimization Algorithm (ECOA), inspired by energy flow and material cycling in natural ecosystems. ECOA is the first metaheuristic to explicitly incorporate three ecological roles—producers, consumers, and decomposers—into its framework, modeling their coevolution and dynamic equilibrium to realize adaptive exploration–exploitation trade-offs in individual update strategies. Evaluated on the CEC2014/2017/2020 benchmark suites and multiple practical engineering optimization problems, ECOA demonstrates superior or competitive performance against state-of-the-art algorithms including PSO, GWO, and SCA, validating its convergence, robustness, and practical applicability. The core contributions lie in (i) ecological role-based functional decomposition modeling and (ii) a cyclically driven, cooperative population search mechanism.

Technology Category

• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in 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 achieve the global optimum, simulating the evolution of an ecological system toward its optimal state of stability and balance. Moreover, the performance of ECO is evaluated against five highly cited algorithms-CS, HS, PSO, GWO, and WOA-on 23 classical unconstrained optimization problems and 24 constrained optimization problems from IEEE CEC-2006 test suite, verifying its effectiveness in addressing various global optimization tasks. Furthermore, 50 recently developed metaheuristic algorithms are selected to form the algorithm pool, and comprehensive experiments are conducted on 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 problems, five state-of-the-art algorithms, including NSM-SFS, FDB-SFS, FDB-AGDE, L-SHADE, and LRFDB-COA, along with four best-performing algorithms from the "CEC2020 competition on real-world single objective constrained optimization", namely SASS, sCMAgES, EnMODE, and COLSHADE, are selected for comparative experiments on five engineering problems from CEC-2020-RW test suite (real-world engineering problems), demonstrating that ECO achieves performance comparable to those of advanced algorithms.

Problem

• Proposes a novel metaheuristic algorithm for global optimization
• Enhances balance between exploration and exploitation processes
• Validates performance on benchmark and real-world engineering problems

Innovation

• Novel metaheuristic algorithm inspired by ecosystem energy flow
• Unique update strategies for producer, consumer, decomposer roles
• Simulates ecological system evolution toward optimal stability balance