## Abstract

In this paper, a new group of optimization algorithms named *Human Strategy Algorithm* (HS) is proposed which is inspired by human strategies to problem solving. The main idea of HS is based on human actions to find the problem’s optima by means of accessible instruments. As the environment of an unknown problem assumed to be a black box, it is supposed that the environment of our problem is a dark room occupied by several men named *blind men*. The main mission of these men is to look for the optimum solution. Each man has at least one instrument as his assistance. Like real life, the instrument might be any tool such as *stick*, *billy*, *rope*, *stone*, *yoyo*, *sweep*. Any instrument by its unique features is suitable in some situations. In fact, this algorithm maps problem space and searches agents to dark room and people, respectively. In this paper, one sample algorithm of the group of human strategy, YOYO Blind Man Algorithm (YOYO-BMA), is introduced which uses yoyos as men’s accessible instruments. The performance of the YOYO-BMA is evaluated on a set of benchmark problems provided for CEC’2010 Special Session and Competition on Large-Scale Global Optimization (Tang et al. 2010). The results show superior performance of proposed algorithm in comparison with others. Moreover, the problem of designing urban traffic network is solve to evaluate the algorithm using a real complex problem.

## Keywords

Human Strategies Algorithm (HS) Blindness operator Optimization YOYO Blind Man Algorithm (YOYO-BMA) Evolutionary algorithm## Notes

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

## References

- Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computationGoogle Scholar
- Back T (1996) Evolutionary algorithm in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, OxfordzbMATHGoogle Scholar
- Back T, Hammel U, Schwefel H-P (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1(1):3–17CrossRefGoogle Scholar
- Bajpai P, Kumar M (2010) Genetic algorithm—an approach to solve global optimization problems. Indian J Comput Sci Eng 1(3):199–206Google Scholar
- Brest J, Zamuda A, Fister I, Maucec S (2010) Large scale global optimization using self-adaptive differential evolution algorithm. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Dai C, Chen W, Zhu Y (2006) Seeker optimization algorithm. In: Proceedings of the international conference on computational intelligence and security, vol 1, pp 225–229, GuangzhouGoogle Scholar
- Darwin C (1859) On the origin of species by means of natural selection; or, the preservation of flavoured races in the struggle for life. John Murray, LondonGoogle Scholar
- Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278MathSciNetCrossRefzbMATHGoogle Scholar
- Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, CambridgezbMATHGoogle Scholar
- Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New JerseyzbMATHGoogle Scholar
- Karaboga D, Basturk B (2007) A powerfull and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Springer Sience+ Business Media, New YorkzbMATHGoogle Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceeding of the IEEE international joint conference on neural networks, IEEE Press, New York, pp 1942–1948Google Scholar
- Korosec K Tashkova, SJ (2010) The differential ant-stigmergy algorithm for large-scale global optimization. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Laarhovan PJM, Arts EHL (1987) Simulated annealing: theory and applications. Kluwer, DordrechtCrossRefGoogle Scholar
- Melanie M (1999) An introduction to genetic algorithms. MIT Press, CambridgezbMATHGoogle Scholar
- Molina D, Lozano M, Herrera F (2010) MA-SW-chains: memetic algorithm based on local search chains for large scale continuous global optimization. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Omidvar MN, Li X, Yao X (2010) Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Tang K, Li X, Suganthan PN, Yang Z, Waise T (2010) Benchmark function for the CEC’2010 special session and competition on large scale global optimization. Technical report, Nature inspired computation and applications laboratory, USTC, ChinaGoogle Scholar
- van den Bergh, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239Google Scholar
- Wang H, Wu Z, Rahnamayan S, Jiang D (2010) Sequantial DE enhanced by neighborhood search for large scale global optimization. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Wang Y, Li B (2010) Two-stage based ensemble optimization for large-scale global optimization. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar
- Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative co-evolution. Inf Sci 178(15):2985–2999CrossRefzbMATHGoogle Scholar
- Yang Z, Tang K, Yao X (2008) Multilevel cooperative co-evolution for large scale optimization. In: Proceedings of the IEEE world congress on computational intelligence (WCCA 2008), IEEE press, New York, pp 1663–1670Google Scholar
- Zhao S-Z, Suganthan PN, Das S (2010) Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search. In: Proceedings of the IEEE world congress on computational intelligence, SpainGoogle Scholar