Bio-inspired optimization algorithms are a set of optimization algorithms inspired by natural phenomena, such as evolutionary processes, social behavior, and swarm intelligence. These algorithms attempt to simulate these processes to solve optimization problems. Classical bio-inspired algorithms include genetic algorithm, ant colony optimization, artificial bee colony, particle swarm optimization, firefly algorithm, Japanese tree frog algorithm, Harris hawks optimizer, etc.



Bio-inspired optimization algorithms can be applied to engineering and sciences in several ways, such as biomarker extraction, image segmentation, disease classification, lesion localization, treatment recommendation, etc.



This special issue plans to report the recent advances in bio-inspired optimization in Engineering and Sciences. The ultimate goal of this special issue is to promote research and development of bio-inspired optimization theories and their applications in engineering and sciences by publishing high-quality research articles and surveys in this rapidly growing interdisciplinary field.



Topics of interest should include, but not be limited to



• Genetic algorithm

• Particle swarm optimization

• Ant colony optimization

• Artificial bee colony

• Firefly algorithm

• Japanese tree frog algorithm

• Harris hawks optimization

• Slime mould algorithm

• Grey wolf optimization

• Sparrow search algorithm

• Whale optimization algorithm

• Bio-inspired algorithms for multi-objective optimization

• Hybrid bio-inspired algorithms

• Parallel and distributed bio-inspired algorithms

• Brain-inspired cognitive architectures