×
Khalil, Ahmed M. E.; Fateen, Seif-Eddeen K.; Bonilla-Petriciolet, Adrián

MAKHA – a new hybrid swarm intelligence global optimization algorithm. (English) Zbl 1461.90112

Algorithms (Basel) 8, No. 2, 336-365 (2015).
Summary: The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.

MSC:

90C26 Nonconvex programming, global optimization
90C59 Approximation methods and heuristics in mathematical programming

Keywords:

global optimization; nature-inspired methods; monkey algorithm; krill herd algorithm; hybridization

Software:

MAKHA; LGO; Krill herd
PDF BibTeX XML Cite
\textit{A. M. E. Khalil} et al., Algorithms (Basel) 8, No. 2, 336--365 (2015; Zbl 1461.90112)
Full Text: DOI
  • logo of hbz
  • logo of WorldCat

References:

[1] Floudas, C.A.; Gounaris, C.E.; A review of recent advances in global optimization; J. Glob. Optim.: 2009; Volume 45 ,3-38. · Zbl 1180.90245
[2] Zhao, R.; Tang, W.; Monkey algorithm for global numerical optimization; J. Uncertain Syst.: 2008; Volume 2 ,165-176.
[3] Gandomi, A.H.; Alavi, A.H.; Krill herd: A new bio-inspired optimization algorithm; Commun. Nonlinear Sci. Numer. Simul.: 2012; Volume 17 ,4831-4845. · Zbl 1266.65092
[4] Ituarte-Villarreal, C.M.; Lopez, N.; Espiritu, J.F.; Using the Monkey Algorithm for Hybrid Power Systems Optimization; Procedia Comput. Sci.: 2012; Volume 12 ,344-349.
[5] Aghababaei, M.; Farsangi, M.M.; Coordinated Control of Low Frequency Oscillations Using Improved Monkey Algorithm; Int. J. Tech. Phys. Probl. Eng.: 2012; Volume 4 ,13-17.
[6] Yi, T.-H.; Li, H.-N.; Zhang, X.-D.; A modified monkey algorithm for optimal sensor placement in structural health monitoring; Smart Mater. Struct.: 2012; Volume 21 .
[7] Yi, T.-H.; Li, H.-N.; Zhang, X.-D.; Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm; Smart Mater. Struct.: 2012; Volume 21 .
[8] Yi, T.H.; Zhang, X.D.; Li, H.N.; Modified monkey algorithm and its application to the optimal sensor placement; Appl. Mech. Mater.: 2012; Volume 178 ,2699-2702.
[9] Sur, C.; Shukla, A.; Discrete Krill Herd Algorithm—A Bio-Inspired Meta-Heuristics for Graph Based Network Route Optimization; Distributed Computing and Internet Technology: New York, NY, USA 2014; ,152-163.
[10] Mandal, B.; Roy, P.K.; Mandal, S.; Economic load dispatch using krill herd algorithm; Int. J. Electr. Power Energy Syst.: 2014; Volume 57 ,1-10.
[11] Zheng, L.; An improved monkey algorithm with dynamic adaptation; Appl. Math. Comput.: 2013; Volume 222 ,645-657. · Zbl 1329.68242
[12] Saremi, S.; Mirjalili, S.M.; Mirjalili, S.; Chaotic krill herd optimization algorithm; Procedia Technol.: 2014; Volume 12 ,180-185.
[13] Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.; A chaotic particle-swarm krill herd algorithm for global numerical optimization; Kybernetes: 2013; Volume 42 ,962-978.
[14] Gharavian, L.; Yaghoobi, M.; Keshavarzian, P.; Combination of krill herd algorithm with chaos theory in global optimization problems; Proceedings of the 2013 3rd Joint Conference of AI & Robotics and 5th RoboCup Iran Open International Symposium (RIOS): Piscataway, NJ, USA 2013; .
[15] Wang, J.; Yu, Y.; Zeng, Y.; Luan, W.; Discrete monkey algorithm and its application in transmission network expansion planning; Proceedings of the Power and Energy Society General Meeting: Piscataway, NJ, USA 2010; .
[16] Wang, G.; Guo, L.; Gandomi, A.H.; Cao, L.; Lévy-flight krill herd algorithm; Math. Probl. Eng.: 2013; .
[17] Guo, L.; Wang, G.-G.; Gandomi, A.H.; Alavi, A.H.; Duan, H.; A new improved krill herd algorithm for global numerical optimization; Neurocomputing: 2014; Volume 138 ,392-402.
[18] Wang, G.; Guo, L.; Wang, H.; Duan, H.; Liu, L.; Li, J.; Incorporating mutation scheme into krill herd algorithm for global numerical optimization; Neural Comput. Appl.: 2014; Volume 24 ,853-871.
[19] Wang, G.-G.; Guo, L.H.; Gandomi, A.H.; Alavi, A.H.; Duan, H.; Simulated annealing-based krill herd algorithm for global optimization; Abstract and Applied Analysis: Cairo, Egypt 2013; . · Zbl 1291.90330
[20] Blum, C.; Roli, A.; Metaheuristics in combinatorial optimization: Overview and conceptual comparison; ACM Comput. Surv.: 2003; Volume 35 ,268-308.
[21] Lozano, M.; García-Martínez, C.; Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report; Comput. Op. Res.: 2010; Volume 37 ,481-497. · Zbl 1173.90590
[22] Liu, P.-F.; Xu, P.; Han, S.-X.; Zheng, J.-Y.; Optimal design of pressure vessel using an improved genetic algorithm; J. Zhejiang Univ. Sci. A: 2008; Volume 9 ,1264-1269. · Zbl 1155.74033
[23] Abdullah, A.; Deris, S.; Mohamad, M.S.; Hashim, S.Z.M.; A New Hybrid Firefly Algorithm for Complex and Nonlinear Problem; Distributed Computing and Artificial Intelligence: Berlin, Germany; Heidelberg, Germany 2012; ,673-680.
[24] Biswas, A.; Dasgupta, S.; Das, S.; Abraham, A.; ; Synergy of PSO and Bacterial Foraging Optimization—A Comparative Study on Numerical Benchmarks Innovations in Hybrid Intelligent Systems: Berlin, Germany; Heidelberg, Germany 2007; ,255-263.
[25] Li, S.; Chen, H.; Tang, Z.; Study of Pseudo-Parallel Genetic Algorithm with Ant Colony Optimization to Solve the TSP; Int. J. Comput. Sci. Netw. Secur.: 2011; Volume 11 ,73-79.
[26] Nguyen, K.; Nguyen, P.; Tran, N.; A hybrid algorithm of Harmony Search and Bees Algorithm for a University Course Timetabling Problem; Int. J. Comput. Sci. Issues: 2012; Volume 9 ,12-17.
[27] Farahani, Sh.M.; Abshouri, A.A.; Nasiri, B.; Meybodi, M.R.; Some hybrid models to improve Firefly algorithm performance; Int. J. Artif. Intell.: 2012; Volume 8 ,97-117.
[28] Kim, D.H.; Abraham, A.; Cho, J.H.; A hybrid genetic algorithm and bacterial foraging approach for global optimization; Inf. Sci.: 2007; Volume 177 ,3918-3937.
[29] Kim, D.H.; Cho, J.H.; A Biologically Inspired Intelligent PID Controller Tuning for AVR Systems; Int. J. Control Autom. Syst.: 2006; Volume 4 ,624-636.
[30] Dehbari, S.; Rosta, A.P.; Nezhad, S.E.; Tavakkoli-Moghaddam, R.; A new supply chain management method with one-way time window: A hybrid PSO-SA approach; Int. J. Ind. Eng. Comput.: 2012; Volume 3 ,241-252.
[31] Zahrani, M.S.; Loomes, M.J.; Malcolm, J.A.; Dayem Ullah, A.Z.M.; Steinhöfel, K.; Albrecht, A.A.; Genetic local search for multicast routing with pre-processing by logarithmic simulated annealing; Comput. Op. Res.: 2008; Volume 35 ,2049-2070. · Zbl 1139.90035
[32] Huang, K.-L.; Liao, C.-J.; Ant colony optimization combined with taboo search for the job shop scheduling problem; Comput. Oper. Res.: 2008; Volume 35 ,1030-1046. · Zbl 1180.90123
[33] Shahrouzi, M.; A new hybrid genetic and swarm optimization for earthquake accelerogram scaling; Int. J. Optim. Civ. Eng.: 2011; Volume 1 ,127-140.
[34] Bäck, T.; Schwefel, H.-P.; An overview of evolutionary algorithms for parameter optimization; Evolut. Comput.: 1993; Volume 1 ,1-23.
[35] Jamil, M.; Yang, X.-S.; A literature survey of benchmark functions for global optimization problems; Int. J. Math. Model. Numer. Optim.: 2013; Volume 4 ,150-194. · Zbl 1280.65053
[36] Mishra, S.K.; Global optimization by differential evolution and particle swarm methods: Evaluation on some benchmark functions, Social Science Research Network, Rochester, NY, USA; ; .
[37] Mishra, S.K.; Some new test functions for global optimization and performance of repulsive particle swarm method, Social Science Research Network, Rochester, NY, USA; ; .
[38] Goldstein, A.; Price, J.; On descent from local minima; Math. Comput.: 1971; Volume 25 ,569-574. · Zbl 0223.65020
[39] Himmelblau, D.M.; ; Applied Nonlinear Programming: New York, NY, USA 1972; .
[40] Ortiz, G.A.; ; Evolution Strategies (ES): Natick, MA, USA 2012; .
[41] Schwefel, H.-P.P.; ; Evolution and Optimum Seeking: The Sixth Generation: Hoboken, NJ, USA 1993; .
[42] Fletcher, R.; Powell, M.J.; A rapidly convergent descent method for minimization; Comput. J.: 1963; Volume 6 ,163-168. · Zbl 0132.11603
[43] Fu, M.C.; Hu, J.; Marcus, S.I.; Model-based randomized methods for global optimization; Proceedings of the 17th International Symposium on Mathematical Theory of Networks and Systems: ; .
[44] Grippo, L.; Lampariello, F.; Lucidi, S.; A truncated Newton method with nonmonotone line search for unconstrained optimization; J. Optim. Theory Appl.: 1989; Volume 60 ,401-419. · Zbl 0632.90059
[45] Oldenhuis, R.P.S.; ; Extended Cube Function: Natick, MA, USA 2009; .
[46] Pintér, J.; ; Global Optimization in Action: Continuous and Lipschitz optimization: Algorithms, Implementations and Applications: Berlin, Germany; Heidelberg, Germany 1995; Volume Volume 6 .
[47] Schumer, M.; Steiglitz, K.; Adaptive step size random search; Autom. Control IEEE Trans.: 1968; Volume 13 ,270-276.
[48] Hartman, J.K.; Some experiments in global optimization; Nav. Res. Logist. Q.: 1973; Volume 20 ,569-576. · Zbl 0265.90048
[49] Griewank, A.O.; Generalized descent for global optimization; J. Optim. Theory Appl.: 1981; Volume 34 ,11-39. · Zbl 0431.49036
[50] Rastrigin, L.; ; Systems of Extremal Control: Moscow, Russia 1974; .
[51] Rosenbrock, H.H.; An automatic method for finding the greatest or least value of a function; Comput. J.: 1960; Volume 3 ,175-184.
[52] Silagadze, Z.; Finding two-dimensional peaks; Phys. Part. Nucl. Lett.: 2007; Volume 4 ,73-80.
[53] ; Towards Global Optimisation 2: Amsterdam, The Netherlands 1978; .
[54] Rahnamayan, S.; Tizhoosh, H.R.; Salama, M.M.; A novel population initialization method for accelerating evolutionary algorithms; Comput. Math. Appl.: 2007; Volume 53 ,1605-1614. · Zbl 1152.90672
[55] Dolan, E.D.; Moré, J.J.; Benchmarking optimization software with performance profiles; Math. Program.: 2002; Volume 91 ,201-213. · Zbl 1049.90004
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.