Fish search algorithm or fish school search (FSS) algorithm is a family of algorithms suited for optimization in high-dimensional search spaces. All fish perform local search and the school aggregates social information.
Following are excerpts from the website of FSS (Fish School Search) Algorithms:
Principles of FSS?
(i) Simple computations in all individuals (i.e. fish)
(ii) Various means of storing information (i.e. weights of fish and school barycenter)
(iii) Local computations (i.e. swimming is composed of distinct components)
(iv) Low communications between neighboring individuals (i.e. fish are to think local but also be socially aware)
(v) Minimum centralized control (mainly for self-controlling of the school radius)
(vi) Some distinct diversity mechanisms (this to avoid undesirable flocking behavior)
(vii) Scalability (in terms of complexity of the optimization/search tasks)
(viii) Autonomy (i.e. ability to self control functioning)
Why using FSS?
FSS is fast, it outperforms most other Swarm Intelligent algorithms and is computationally inexpensive. New investigations also show that FSS is easy to go-GPU.
Principles of FSS?
(i) Simple computations in all individuals (i.e. fish)
(ii) Various means of storing information (i.e. weights of fish and school barycenter)
(iii) Local computations (i.e. swimming is composed of distinct components)
(iv) Low communications between neighboring individuals (i.e. fish are to think local but also be socially aware)
(v) Minimum centralized control (mainly for self-controlling of the school radius)
(vi) Some distinct diversity mechanisms (this to avoid undesirable flocking behavior)
(vii) Scalability (in terms of complexity of the optimization/search tasks)
(viii) Autonomy (i.e. ability to self control functioning) Rationale?
(1) 'Swimming' actually is a means of:
Performing a local search
Storing information of success
Indirectly conveying social information
(2) Success of the search is given by:
Fish weights (large is better)
School radius (small is better - meaning more heavy fishes)
School barycenter (closer to optima is better)
(3) Non-monotonicity is achieved, e.g.:
By random hesitation before swim
By expansion/shrinking the school radius
By variations on swimming components
http://www.fbln.pro.br/fss/
The Fish School Search (FSS) is another optimization algorithm based on the gregarious behavior of schools. In the FSS, each fish represents a solution for the problem and the success of a fish during the search process is indicated by its weight. The FSS has four operators, which are executed for each fish of the school at each iteration: (i) individual movement; (ii) feeding; (iii) collective-instinctive movement; and (iv) collective-volitive movement. Since we will use only the feeding and collective-volitive movement operators in our proposal, we detail them in the following subsections.
Here are three recent articles:
Boulkabeit, I., Mthembu, L., Marwala, T., & Neto, F. B. D. L. (2013, September). Finite Element Model Updating Using Fish School Search Optimization Method. 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on (pp. 447-452). IEEE.
http://arxiv.org/pdf/1308.2307
Cavalcanti-Júnior, G. M., Bastos-Filho, C. J., Lima-Neto, F. B., & Castro, R. M. (2011). A hybrid algorithm based on fish school search and particle swarm optimization for dynamic problems. In Advances in Swarm Intelligence (pp. 543-552). Springer Berlin Heidelberg.
http://www.posgraduacao.poli.br/sistemapesquisa/files/auxilios/inscricao/VolitivePSO.pdf
Madeiro, S. S., de Lima-Neto, F. B., Bastos-Filho, C. J. A., & do Nascimento Figueiredo, E. M. (2011). Density as the segregation mechanism in fish school search for multimodal optimization problems. In Advances in Swarm Intelligence (pp. 563-572). Springer Berlin Heidelberg.
http://arxiv.org/pdf/1308.2307
http://www.posgraduacao.poli.br/sistemapesquisa/files/auxilios/inscricao/VolitivePSO.pdf
Conference Paper Density as the Segregation Mechanism in Fish School Search f...
A recent IEEE paper (2005) based on Fish Search Algorithm:
"An improved "fish-search" algorithm for information retrieval"
by Fang-fang Luo ; Fuzhou Univ., China ; Guo-Long Chen ; Wen-Zhong Guo
Abstract:
The World-Wide Web (WWW) based on Internet services has become a major channel for information delivery. Therefore, a scalable approach to support Internet searching is critical to the success of Internet services. Actually Internet is a directed graph, webpage as node and hyperlink as edge, so the search operation could be abstracted as a process of traversing directed graph. This paper introduces a famous dynamic Web search algorithms, the "fish-search", and analyzes its merits contrasting to general depth-first algorithm, and points out that the random of search range could lead to repeated search or overlong search time. Based on the analysis of flocking behavior offish school, a parameter "dist" is defined to control the search range and time. This paper presents an improved "fish-search" algorithm, in which the search process could adjust the search range adaptively. Simulation shows some improvements over the original fish-search algorithm.
Published in:
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on Natural Language Processing (30 Oct.-1 Nov. 2005), pp. 523-528, (IEEE).
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1598793&reason=concurrency
The Fish School Search (FSS) is another optimization algorithm based on the gregarious behavior of schools. In the FSS, each fish represents a solution for the problem and the success of a fish during the search process is indicated by its weight. The FSS has four operators, which are executed for each fish of the school at each iteration: (i) individual movement; (ii) feeding; (iii) collective-instinctive movement; and (iv) collective-volitive movement. Since we will use only the feeding and collective-volitive movement operators in our proposal, we detail them in the following subsections.
Here are three recent articles:
Boulkabeit, I., Mthembu, L., Marwala, T., & Neto, F. B. D. L. (2013, September). Finite Element Model Updating Using Fish School Search Optimization Method. 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on (pp. 447-452). IEEE.
http://arxiv.org/pdf/1308.2307
Cavalcanti-Júnior, G. M., Bastos-Filho, C. J., Lima-Neto, F. B., & Castro, R. M. (2011). A hybrid algorithm based on fish school search and particle swarm optimization for dynamic problems. In Advances in Swarm Intelligence (pp. 543-552). Springer Berlin Heidelberg.
http://www.posgraduacao.poli.br/sistemapesquisa/files/auxilios/inscricao/VolitivePSO.pdf
Madeiro, S. S., de Lima-Neto, F. B., Bastos-Filho, C. J. A., & do Nascimento Figueiredo, E. M. (2011). Density as the segregation mechanism in fish school search for multimodal optimization problems. In Advances in Swarm Intelligence (pp. 563-572). Springer Berlin Heidelberg.
http://arxiv.org/pdf/1308.2307
http://www.posgraduacao.poli.br/sistemapesquisa/files/auxilios/inscricao/VolitivePSO.pdf
Conference Paper Density as the Segregation Mechanism in Fish School Search f...
https://www.researchgate.net/publication/297245624_Particle_Swarm_Optimization_Algorithm_and_its_Codes_in_MATLAB
Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB
https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization
Code Codes in MATLAB for Particle Swarm Optimization
Recent articles:
Serapião, A. B., Corrêa, G. S., Gonçalves, F. B., & Carvalho, V. O. (2016). Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units. Applied Soft Computing.
Boulkaibet, I., Mthembu, L., De Lima Neto, F., & Marwala, T. (2015). Finite element model updating using fish school search and volitive particle swarm optimization. Integrated Computer-Aided Engineering, 22(4), 361-376.
Jena, S. (2013). Fish school search: an interval representation (Doctoral dissertation, National Institute of Technology, Rourkela).
Bastos-Filho, C. J., & Guimarães, A. C. (2015). Multi-Objective Fish School Search. International Journal of Swarm Intelligence Research (IJSIR), 6(1), 23-40.
Article Multi-Objective Fish School Search
Conference Paper A novel search algorithm based on fish school behavior
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