A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global...

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Metaheuristic Computing

Background:

• The Marine Predators Algorithm (MPA) is an efficient metaheuristic inspired by marine predator-prey foraging strategies.
• MPA exhibits limitations including low solution diversity, tendency towards local optima, and reduced convergence speed on complex problems.

Purpose of the Study:

• To address the limitations of the original MPA.
• To propose a novel enhanced algorithm, ODMPA, with improved exploration and convergence capabilities.

Main Methods:

• The proposed ODMPA integrates the tent map for increased search agent diversity.
• A differential evolution mutation with simulated annealing (DE-SA) mechanism is incorporated to boost exploration.
• An outpost mechanism is employed to accelerate the convergence speed of the algorithm.

Main Results:

• ODMPA achieved superior performance compared to existing algorithms on the IEEE CEC2014 benchmark functions.
• The modified algorithm demonstrated higher accuracy in solving complex real-world optimization problems, including engineering tasks and photovoltaic model parameter identification.
• Empirical results validate the effectiveness of the introduced mechanisms in enhancing MPA's performance.

Conclusions:

• The proposed ODMPA effectively overcomes the drawbacks of the original MPA.
• ODMPA shows significant potential as a versatile and effective tool for a wide range of optimization challenges.