MosquitoSwarm: Bio-Inspired Collective Intelligence for Multi-Objective Optimization in Computational Sciences

Keywords: swarm intelligence, bio-inspired computing, mosquito behavior, multi-objective optimization, collective intelligence, computational biology, evolutionary algorithms, emergence

Abstract: Mosquito swarms exhibit sophisticated collective behaviors that have evolved over millions of years to solve complex multi-objective optimization problems including resource discovery, predator avoidance, and reproductive success. Despite their biological significance, mosquito swarm intelligence remains largely unexplored in computational sciences. We introduce MosquitoSwarm, a novel bio-inspired optimization framework that captures the unique behavioral patterns of mosquito colonies, including their multi-layered communication protocols, adaptive foraging strategies, and emergent decision-making processes. Our approach models three key mosquito behaviors: (1) chemical gradient following with noise-resistant navigation, (2) collective threat response with distributed alarm systems, and (3) adaptive resource allocation based on environmental feedback. Through rigorous mathematical analysis, we establish convergence properties and demonstrate superior performance on benchmark optimization problems. Extensive experiments across protein folding, neural architecture search, and climate modeling show consistent improvements of 20-40% over existing swarm intelligence methods. The framework reveals emergent problem-solving strategies that mirror natural mosquito colony intelligence, providing new insights into distributed optimization and collective decision-making in biological systems.

Submission Number: 225