International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.8, No.11, Nov. 2016
Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization
Full Text (PDF, 754KB), PP.34-50
Krill herd algorithm (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, moment induced by other krill, foraging motion and random physical diffusion. These three key features of the algorithm provide a good balance between global and local search capability, which makes the algorithm very powerful. The objective is to minimize the distance of each krill from the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH Algorithm. KHA with mutation and crossover is called as (KHAMC) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two improved variants KHAMC and KHAMCD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms.
Cite This Paper
Prasun Kumar Agrawal, Manjaree Pandit, Hari Mohan Dubey,"Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.11, pp.34-50, 2016. DOI: 10.5815/ijisa.2016.11.05
Holland J H. Genetic algorithms and the optimal allocation of trials. SIAM J. Comput., 1973, 2(2): 88-105.
Beyer H G, Schwefel H P. Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing, 2002, 1 (1): 3–52.
Fogel L J, Owens A J, Walsh M J. Artificial intelligence through simulated evolution. 1966, Wiley, New York.
Reynolds R G. An introduction to cultural algorithms. In Proceedings of the 3rd annual conference on evolutionary programming, 1994. 131–139.
Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing. Science, 1983, 220(4598):671–680.
Glover F. Tabu search. Part I. ORSA Journal on Computing, 1989, 1(3):190–206.
Kennedy J, Eberhart R C. Particle swarm optimization. In Proceedings of IEEE international conference on neural networks, Piscataway, NJ, 1995. 1942–1948.
Dorigo M, Maniezzo V, Colorni A. The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B: Cybern., 1996, 26:29–41.
Geem Z W, Kim J H, Loganathan G. A new heuristic optimization algorithm: harmony search. Simulation, 2001,76(2): 60–68.
Ocenasek J, Schwarz J. Estimation of distribution algorithm for mixed continuous-discrete optimization problems. In 2nd Euro-International Symposium on Computational Intelligence, 2002. 227–232.
Passino K M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag., 2002, 22(3): 52–67.
Haddad O B, Afshar A, Marin o M A. Honey bees mating optimization algorithm (HBMO): a new heuristic approach for engineering optimization. Proceeding of the First International Conference on Modeling, Simulation and Applied Optimization, 2005.
Shah-Hosseini H. The intelligent water drops algorithm: A nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation, 2009, 1(1/2): 71–79.
Yang X-S. Firefly algorithms for multimodal optimization. Stochastic Algorithms, Foundations and Applications. Springer, 2009. 169–178.
Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim, 2007, 39: 459-471.
Rashedi E, N-pour H, Saryazdi S. GSA: A Gravitational search algorithm. Information Sciences, 2009, 179: 2232-2248.
Yang X-S. A new metaheuristic bat-inspired algorithm. In J. R. Gonzalez et al. (Eds.), Nature inspired cooperative strategies for optimization (NISCO 2010). Studies in computational intelligence, Berlin:. Springer. 2010, 284: 65–74.
Rajabioun R. Cuckoo optimization algorithm. Applied Sof Computing, 2011,11: 5508-5518.
Gandomi A H, Alavi A H. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications In Nonlinear Science And Numerical Simulation, 2012, 17: 4831–4845.
Cuevas E, Cienfuegos M. A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Systems with Applications, 2014, 41: 412-425.
Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and computation, 2013, 219(15): 8121-8144.
Mirjalili S, Mirjalili S M, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software, 2014, 69: 46-61.
Cheng M-Y, Prayogo D. Symbiotic Organism Search: A metaheuristic optimization algorithm. Computers and Structures, 2014, 139: 98-112.
Yazdani M, Jolai F. Lion optimization algorithm. Journal of Computational Design and Engineering, 2015, doi:10.1016/j.jcde.2015.06.003
Salimi H. Stochastic Fractal search: a powerful metaheuristic algorithm. Knowledge based systems, 2015, 75: 1-18.
Shareef H, Ibrahim A A, Mutlag A H. Lightning search algorithm[J]. Applied Soft Computing, 2015, 36: 315-333.
Wolpert D H, Macready W G. No free lunch theorems for optimization . IEEE Trans. Evol. Comput., 1997, 1 (1): 67–82,1997.
Marr J W S. The natural history and geography of the Antarctic krill (Euphausia superba Dana). Disc Rep, 1962, 32:33–464.
Wang G-G, Deb S, Gandomi A H, Alavi A H. Opposition based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing, 2015, dx.doi.org/10.1016.
Nikbakht H, Mirvaziri H. A new clustering approach on K-means and krill herd algorithm. 23rd Iranian conference on Electrical Engineering, 2015. 662-667.
Pandey S, Patidar R, George N V. Design of a krill herd algorithm based adaptive channel equalizer. International Symposium on Intelligent Signal Processing and Communication System, 2014. 257-260.
Saremi S, Mirjalili S M, Mirjalili S. Chaotic Krill Herd Optimization Algorithm. Procedia Technology, 2014, 12: 180-185.
Bidar M, Fattahi E, Kanan H R. Modified krill herd optimization algorithm using chaotic parameters. 4th International Conference on Computer and Knowledge Engineering, 2014. 420-424.
Wang G-G, Gandomi A H, Alavi A H. Stud krill herd algorithm. Neurocomputing, 2014, 128: 363-370.
Wang G-G, Gandomi A H, Alavi A H. An effective krill herd algorithm with migration operator in biogeography-based optimization. Applied Mathematical Modeling, 2014, 38: 2454-2462.
Fattahi E, Bidar M, Kanan H R. Fuzzy krill herd optimization algorithm. IEEE first International Conference on Networks and Soft Computing, 2014. 423-426.
Rodrigues D, Pereira L A M, Papa P J, Weber S A T. A Binary krill herd approach for feature selection. 22nd International Conference on Pattern Recognition, 2014. 1407-1412.
Li J, Tang Y, Hua C, Guan X. An improved krill algorithm: Krill herd with linear decreasing step. Applied Mathematics and Computation, 2014, 234: 356-367.
Jamil M, Yang X-S. A literature survey of benchmark functions for global optimization problems. Int. Journal of Mathematical Modeling and numerical optimization, 2013, 4: 150-194.
Dubey H M, Panigrahi B K, Pandit M. Bio-inspired optimization for economic load dispatch: a review, International Journal of Bio-Inspired Computation 2014, 6 (1), 7-21.
Wood A J, Wallenberg B F. Power Generation, Operation and Control. 1984, New York: Wiley.
Ciornei I, Kyriakides E. A GA-API Solution for the Economic Dispatch of Generation in Power System Operation. IEEE Trans Power Syst., 2012, 27 (1).
Reddy A S, Vaisakh K. Shuffled differential evolution for large scale economic dispatch. Electric Power Systems Research, 2013, 96: 237-45, .
Roy P K, Bhui S. Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem. Int. J. Electr. Power Energy Syst., 2013, 53: 937–48.
Bhattacharjee K, Bhattacharya A, Halder nee Dey S. Oppositional Real Coded Chemical Reaction Optimization for different economic dispatch problems. Electrical Power and Energy Systems, 2014, 55: 378–391.
Mandal B, Roy P K, Mandal S. Economic load dispatch using krill herd algorithm. Electrical Power and Energy Systems, 2014, 57: 1–10.
Barisal A K, Prusty R C. Large scale economic dispatch of power systems using oppositional invasive weed optimization. Applied Soft Computing, 2015, 29: 122–137.
Dubey H M, Pandit M, Panigrahi B K, Udgir M. Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm. I J Intelligent Systems and Applications, 2013, 5(8): 21–32.
Dubey H M, Pandit M, Panigrahi B K. A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cognitive Computation, 2015, 7(5): 594-608.
Mahdad B, Srairi K.. Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method. I.J. Intelligent Systems and Applications, 2014, 07(6): 36-43.
Labbi Y, Attous D B, Gabbar H A, Mahdad B, Zidan A. A new rooted tree optimization algorithm for economic dispatch with valve-point effect. Electrical Power and Energy Systems 79 (2016) 298–311.
D. Bisen, H. M. Dubey, M. Pandit, B.K. Panigrahi, solution of large scale economic load dispatch problem using quadratic programming and GAMS: A comparative analysis, Journal of Information and Computing science, vol. 7,no.3,pp.200-2011,2012.
Ali Bulbul S M, Pradhan M, Roy P K, T. Pal. Opposition-based krill herd algorithm applied to economic load dispatch problem. Ain Shams Engineering Journal, .doi.org/10.1016/j.asej.2016.02.003
Parouha R P, Das K N. A novel hybrid optimizer for solving Economic Load Dispatch problem. Electrical Power and Energy Systems, 2016, 78:108–126.
Singh G P, .Singh A. Comparative Study of Krill Herd, Firefly and Cuckoo Search Algorithms for Unimodal and Multimodal Optimization. I.J. Intelligent Systems and Applications, 2014, 03:35-49