Nature-Inspired Optimization Techniques

Session Chairs

Prof.(Dr.) Satya Bir Singh Punjabi University, India E-mail: sbsingh69@yahoo.com	Dr. Narinder Singh Punjabi University, India E-mail: narindersinghgoria@gmail.com	Dr. Mehar Chand Baba Farid College, India E-mail: mehar.jallandhra@gmail.com

Aim & Scope

The aims and scope of this special session is to bring together academicians, researchers, professionals, executives and practicing engineers from various industries, research institutes and educational bodies to share, review, exchange ideas and discuss current developments in nature inspired meta-heuristics. These nature-inspired optimization algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Developing economies, aspiration for improved quality of life and rise in the rate of growth have led to the development of various infrastructure facilities in the society. This session offers a real opportunity to discuss new issues, tackle complex problems and find advanced enabling solutions, which are able to shape new trends in Computer Science, Engineering, Optimization and Bio-Medical sciences for the development of human mankind being as a whole. Hopefully, these discussions will open a debate on new opportunities and new challenges in the area, unifying the efforts toward the development of new adequate tools, protocols and databases for evaluating and monitoring the progress in area of interest. This special session welcomes original and unpublished contributions on all aspects of nature-inspired optimization algorithms, including (but not limited to):

• Algorithms such as particle swarm optimization, ant and bee algorithms, butterfly optimization algorithm, firefly algorithm, bat algorithm, flower algorithm, cuckoo search, Sine Cosine, Chimp Optimizer etc. • Modeling and analysis of new nature-inspired optimization algorithms

• Hybrid schemes with other algorithms

• Theoretical aspects of nature-inspired optimization algorithms

• New benchmarks and performance measures to evaluate nature-inspired optimization algorithms

• Applications to both constrained and unconstrained optimization, single-objective, multi-objective and many-objective optimization, static or dynamic problems and real-world optimization problems.