Comprehensive Taxonomies of Nature- and Bio-inspired Optimization Algorithms: Inspiration, Behavior, Critical Analysis, and Recommendations (2020-2024)

Core Concepts

This study presents two comprehensive taxonomies to classify nature- and bio-inspired optimization algorithms based on their source of inspiration and algorithmic behavior. The analysis reveals a poor relationship between the natural inspiration and the actual behavior of many algorithms, with over a quarter being versions of classical algorithms. The study provides recommendations to improve research practices in this field.

Abstract

This study proposes two taxonomies to classify nature- and bio-inspired optimization algorithms: Taxonomy by Source of Inspiration: Breeding-based Evolutionary Algorithms: Inspired by natural evolution and breeding processes. Swarm Intelligence based Algorithms: Further divided into subcategories based on the type of animals (flying, terrestrial, aquatic, microorganisms) and the specific behaviors (foraging, movement) that inspired the algorithms. Physics and Chemistry Based: Inspired by physical and chemical processes. Social Human Behavior Algorithms: Inspired by human social behaviors. Plants Based: Inspired by plant-based processes. Miscellaneous: Algorithms that do not fit into the other categories. Taxonomy by Algorithmic Behavior: The algorithms are classified based on how they generate new candidate solutions, without considering the source of inspiration. The study then analyzes the relationships between the two taxonomies, revealing that the natural inspiration of many algorithms does not align well with their actual algorithmic behavior. Over a quarter of the reviewed algorithms are found to be versions of classical algorithms, despite claiming novel bio-inspired origins. The authors provide several recommendations to improve research practices in this field, such as better justification of novelty, rigorous comparisons with state-of-the-art, and a focus on solving real-world optimization problems rather than proposing new metaphor-based algorithms.

Stats

"The growing number of nature-inspired proposals could be seen as a symptom of the active status of this field; however, its sharp evolution suggests that research efforts should be also invested towards new behavioral differences and verifiable performance evidence in practical problems." "More than one-fourth of the reviewed bio-inspired solvers are versions of classical algorithms."

Quotes

"It is our firm belief that this controversy could be lessened by a comprehensive taxonomy of nature and bio-inspired optimization algorithms that settled the criteria to justify the novelty and true contributions of current and future advances in the field." "The conclusions from the analysis of the algorithms lead to several learned lessons."

Key Insights Distilled From

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)

by Dani... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2002.08136.pdf

Deeper Inquiries

How can the research community better evaluate the true novelty and contributions of new nature- and bio-inspired optimization algorithms

To better evaluate the true novelty and contributions of new nature- and bio-inspired optimization algorithms, the research community can implement the following strategies: Establish Clear Evaluation Criteria: Define specific criteria for evaluating the novelty of algorithms, such as the uniqueness of the biological inspiration, the effectiveness in solving complex problems, and the efficiency in comparison to existing algorithms. Comparative Analysis: Conduct thorough comparative analyses with existing algorithms to highlight the distinct features and improvements of the new proposals. Real-World Applications: Emphasize the application of new algorithms to real-world problems to demonstrate their practical utility and effectiveness. Peer Review: Encourage peer review processes that focus on assessing the novelty and contributions of the algorithms based on established criteria. Publication Standards: Enforce publication standards that require authors to clearly articulate the novelty and contributions of their algorithms in relation to existing literature.

What are the potential drawbacks of the current trend of proposing new metaphor-based algorithms without a focus on solving real-world problems

The current trend of proposing new metaphor-based algorithms without a focus on solving real-world problems can lead to several potential drawbacks: Lack of Practical Utility: Algorithms developed solely based on metaphors may not effectively address real-world optimization challenges, limiting their practical utility. Limited Impact: Without a focus on solving real problems, the algorithms may have limited impact on advancing the field of optimization and may not contribute significantly to practical applications. Repetitive Research: Continual development of metaphor-based algorithms without tangible applications can lead to redundant research efforts and a lack of progress in the field. Difficulty in Evaluation: It becomes challenging to evaluate the true effectiveness and contributions of algorithms that are not tested in real-world scenarios, leading to ambiguity in their value. Misalignment with Research Goals: The divergence from addressing real problems may deviate from the primary goal of optimization research, which is to develop solutions that have practical significance and impact.

How can the design and application of nature- and bio-inspired optimization algorithms be better integrated with the broader field of artificial intelligence and machine learning

Integrating the design and application of nature- and bio-inspired optimization algorithms with the broader field of artificial intelligence and machine learning can be enhanced through the following approaches: Interdisciplinary Collaboration: Foster collaboration between researchers in optimization, AI, and machine learning to leverage expertise from diverse fields and enhance the development of hybrid algorithms. Cross-Domain Applications: Explore applications of nature-inspired optimization algorithms in various AI domains such as computer vision, natural language processing, and reinforcement learning to showcase their versatility and effectiveness. Algorithm Fusion: Integrate nature-inspired optimization techniques with deep learning frameworks to create hybrid algorithms that combine the strengths of both approaches for improved performance. Benchmarking and Evaluation: Establish standardized benchmarks and evaluation metrics that allow for a fair comparison between nature-inspired algorithms and traditional AI methods, facilitating their integration into mainstream AI research. Educational Initiatives: Promote educational programs and workshops that highlight the synergies between nature-inspired optimization and AI, encouraging researchers to explore the intersection of these fields for innovative solutions.