Nature-Inspired-Optimization

Nature Inspired Optimization is a type of metaheuristic algorithm(compared to deterministic algorithm), which is a method to generate acceptable solutions for complicated problems in a reasonably practical time, although there is no guarantee that optimal solutions can be attained. In addition, Nature Inspired Optimization focuses on producing feasible solutions originating from the nature inspiration method, including swarm intelligence, creature behaviors, and evolutionary intelligence during the optimization iteration.

Outline

1. Particle Swarm Optimization
   • Introduction
   • Code
2. Quantum Particle Swarm Optimization
   • Introduction
   • Code
3. Nelder-Mead Optimization
   • Introduction
   • Code
4. Hybrid Method Optimization
   • Introduction
   • Code
5. Flower Pollinat Algorithm
   • Introduction
   • Code
6. Reference

Getting Started

Particle Swarm Optimization

(1-1) Introduction

(1-2) Code

• Particle Swarm Optimization with Nearest method (constrain treatment)

• Particle Swarm Optimization with Reflection method and velocity zero (constrain treatment)

Quantum Particle Swarm Optimization

(2-1) Introduction

(2-2) Code

• Quantum Particle Swarm Optimization with Nearest method (constrain treatment)

Nelder-Mead Optimization

(3-1) Introduction

(3-2)Code

• Nelder-Mead unconstrained

• Nelder-Mead with Nearest method (constrain treatment)

Hybrid Method Optimization

(4-1) Introduction

(4-2)Code

• Nelder-Mead & Particle Swarm Optimization with Standard Deviation(stopping criterion)

• [Nelder-Mead & Particle Swarm Optimization with Woods(stopping criterion)

Flower Pollinat Algorithm

(5-1) Introduction

(5-2)Code

• Flower Pollination Algorithm

Reference

• [1]:Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, NJ, USA; 1995.

• [2]:Banks, A., Vincent, J. & Anyakoha, C. Nat Comput (2008) 7: 109. https://doi.org/10.1007/s11047-007-9050-z

• [3]:Sabine Helwig, Juergen Branke, and Sanaz Mostaghim, "Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization", April 2013 IEEE Transactions on Evolutionary Computation 17(2):259 - 271

• [4]: Bin Feng, Jun Sun, and Wenbo Xu, "Particle Swam Optimization with Particles Having Quantum Behavior", July 2004 IEEE

• [5]: J. Sun et al, "A Global Search Strategy of Quantum-behaved Particle Swarm Optimization", Proc. 2004 IEEE Conference on Cybernetics and Intelligent Systems.

• [6]: Sun J., Xu W., Fang W. (2006) "Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity" In: Alexandrov V.N., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg

• [7]: W. Spendley, G.R. Hext, F.R. Himsworth, "Sequential application of simplex designs in optimization and evolutionary operation"", Technometrics 4 (1962) 441-461.

• [8]: M, Baudin, "Nelder-Mead User's Manual"", 2010.

• [9]: Ellen Fan. "Global optimization of lennard-jones atomic clusters". Technical report, McMaster University, February 2002

• [10]: Erwie Zahara, Yi-Tung Kao, and Chia-Hsin Hu "Hybrid simplex search and particle swarm optimization for unconstrained optimization", February 2007 European Journal of Operational Research 181(2):527-548 DOI: 10.1016/j.ejor.2006.06.034

• [11]: S. Smith, The simplex method and evolutionary algorithms, in: Proceedings of the IEEE International Conference on Evolutionary Computation 1998, pp. 799-804.

• [12]: R. Hooke, T.A. Jeeves, Direct search solution of numerical and statistical problems, Journal of Association for Computing Machinery 8 (1961) 212-221.

• [13]: J.E. Dennis Jr., D.J. Woods, Optimization on microcomputers: the Nelder-Mead simplex algorithm, in: A. Wouk (Ed.), New Computing Environments: Microcomputers in Large Scale Computing, SIAM, Philadelphia, PA, 1987, pp. 116-122.