Modified Whale Optimization Algorithm with Migration for Search Diversity

Summary

The article describes a modified Whale Optimization Algorithm, a population-based method inspired by humpback whale hunting behavior. Candidate solutions move in relation to the current best solution through exploration and exploitation steps, including leader-directed updates and spiral motion. The modification adds a migration stage that randomly changes candidate positions to maintain diversity and reduce premature convergence. The article outlines the algorithm's parameters, update logic, and implementation in MQL5.

It evaluates the method on several test functions and reports strengths on sharp or discrete landscapes, including Forest and Megacity, alongside weak convergence and poor scalability on smooth, high-dimensional Hilly problems. These are experimental optimization results, not evidence about financial trading performance. The author notes that implementations of canonical algorithms were modified, and that conclusions depend on the reported experiments. The method is presented as a general optimizer that could be applied to search problems, with local optima and convergence speed remaining concerns.

Key ideas

• WOA represents candidate solutions as agents that update their positions using the best solution and search rules.
• The method balances exploration and exploitation through leader-based movement and spiral updates.
• The modified version adds random migration to increase diversity and reduce the risk of local trapping.
• Tests report stronger performance on some sharp or discrete functions and weaker results on smooth, high-dimensional cases.
• The reported experiments assess optimization benchmarks, not profitability in trading applications.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.