During last decade, the nature has inspired researchers to develop new algorithms [1, 2, 3]. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees algorithm, bat algorithm, firefly algorithm etc.), genetic and evolutionary strategies, artificial immune systems etc. As well-known examples, the following have to be mentioned: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based on collective social behavior of organisms, researchers had developed optimization strategies taking into account not only the individuals, but also groups and environment [1]. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms. After a short introduction, this work review the most effective, according to their performance, nature-inspired algorithms, in the second section. The third section is dedicated to learning strategies based on nature oriented thinking. Examples and the benefits obtained from applying nature-inspired strategies in problem solving are given in the fourth section. Concluding remarks are given in the final section. References 1. G. Albeanu, B. Burtschy, Fl. Popentiu-Vladicescu, Soft Computing Strategies in Multiobjective Optimization, Ann. Spiru Haret Univ., Mat-Inf Ser., 2013, 2, http://anale-mi.spiruharet.ro/upload/full_2013_2_a4.pdf 2. H. Madsen, G. Albeanu, and Fl. Popentiu-Vladicescu, BIO Inspired Algorithms in Reliability, In H. Pham (ed.) Proceedings of the 20th ISSAT International Conference on Reliability and Quality in Design, Reliability and Quality in Design, August 7-9, 2014, Seattle, WA, U.S.A. 3. N. Shadbolt, Nature-Inspired Computing, http://www.agent.ai/doc/upload/200402/shad04_1.pdf