Optimization of Ecological Systems and Population Dynamics Using Metaheuristic Algorithms: A Review

Abstract

Ecological optimization combines a search problem with a model of populations, habitats or resource allocation. This narrative review examines how metaheuristic algorithms can support parameter calibration, conservation planning and management decisions while preserving the distinction between optimizer performance and ecological validity. It organizes search methods by representation and evaluation cost, and discusses simulated annealing, evolutionary search and population-based continuous optimization through their mathematical roles. The Marxan conservation-planning framework provides a documented example of spatial selection with cost, boundary and representation penalties. Population-model calibration is treated as an inverse problem whose identifiability, observation process and predictive validation must be established independently of the search method. The review considers matched benchmarking, stochastic replication, scenario evaluation and transparent constraint handling. Unverified literature counts and quantitative graphics are excluded from the validated synthesis. No new population experiment, pooled performance estimate or universal algorithm ranking is reported. Research priorities include process-based benchmarks, independent spatial and temporal validation, explicit treatment of management uncertainty and reproducible decision-support workflows.