Nature-Inspired Metaheuristics for Solving Complex Optimization Problems in the Maritime Industry
TL;DR
Recent algorithms such as The Pelican Optimization Algorithm, The Squirrel Search Optimization Algorithm, The Snow Leopard Optimization Algorithm and The Deer Hunting Optimization Algorithm have outperformed traditional metaheuristics in optimising known test functions, suggesting that they may offer promising improvements for solving complex maritime optimization problems.
Abstract
The maritime industry is crucial for global trade and the promotion of economic development. However, it faces major challenges, especially in tackling complex NP-hard problems that require effective decision support. To overcome these challenges, nature-inspired metaheuristic algorithms have proven to be powerful tools for optimization processes in the maritime sector. This paper provides a systematic overview of the application of these algorithms to various maritime problems, including collision avoidance, ship routing, tugboat scheduling, evacuation planning, container stowage, berth allocation, quay crane allocation, transport optimisation, search and rescue operations, hydrant location optimization and the damage stability problem. In addition, new optimisation algorithms are constantly being presented in the literature to further improve performance in various applications. As shown in this paper, recent algorithms such as The Pelican Optimization Algorithm, The Squirrel Search Optimization Algorithm, The Snow Leopard Optimization Algorithm and The Deer Hunting Optimization Algorithm have outperformed traditional metaheuristics in optimising known test functions, suggesting that they may offer promising improvements for solving complex maritime optimization problems.