Enhancing Marine Comprehensive Carrying Capacity and Energy Assessment and Prediction Using an Improved Ant Colony Algorithm and System Dynamics Model

Abstract

The primary aim of this paper is to introduce a novel approach to simulating and predicting Marine Comprehensive Carrying Capacity (MCCC), which seeks to enhance the efficacy and accuracy of MCCC assessment and prediction. MCCC is crucial for effective marine resource management and sustainable energy exploitation, as it determines the maximum activities that the marine environment can support without significant degradation. Given the considerable complexity associated with the marine environment and the need for more reliable predictive technologies, this paper proposes an integrated model that combines the capabilities of the proven optimization algorithm, Enhanced Ant Colony, and System Dynamics Modelling. This approach allows for detailed simulation of the variables associated with MCCC, improving prediction precision.The study details the methodology for developing an adapted Ant Colony algorithm and the foundation of a system dynamics model. These models are interconnected within a single framework, tested across multiple scenarios to validate their robustness and sustainability. The results demonstrate the superiority of the proposed approach over conventional models in terms of prediction accuracy and precision, confirmed through both in-sample and out-of-sample validation procedures.This paper is a significant contribution to the fields of sustainability and energy management within marine environments. It provides a new tool for policymakers and environmental managers to enhance their decision-making processes with a greater depth of knowledge, ensuring the sustainable utilization of marine resources and energy potential.

Keywords

• Marine Comprehensive Carrying Capacity
• Ant Colony Algorithm
• System Dynamics
• Environmental Management
• Sustainable Marine Development