A Critical Analysis of Raven Roost Optimization
Martijn Halsema, Diederick Vermetten, Thomas Bäck, Bas van Stein · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
This study critically examines the Raven Roost Optimization (RRO) algorithm within the broader context of nature-inspired metaheuristics, challenging its novelty and efficacy in the field of black-box optimization. Many similar methods use ideas from nature, but it's important to see if they really bring something new to the table. We compared RRO with another well-known method called Particle Swarm Optimization (PSO) to see how well RRO works and if it's truly a new idea. Through comprehensive analysis and benchmarking, we reveal that RRO's purported novelty largely recapitulates existing strategies under a new metaphorical guise. The algorithm's performance is systematically evaluated across various dimensions, revealing inherent limitations and biases introduced by its metaphorical foundation. Our findings advocate for a critical reassessment of metaphor-based heuristics, urging the computational intelligence community to prioritize substantive algorithmic advancements over superficial novelty. The call for rigorous evaluation and validation of new optimization methods is underscored, emphasizing the need for transparency, reproducibility, and genuine innovation in algorithmic design. This work contributes to the ongoing discourse on the validation and merit of bio-inspired algorithms, providing insights that may guide future research towards more meaningful and empirically justified contributions.