Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions

Abstract This paper proposes a novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm. Like other evolutionary algorithms, the proposed algorithm starts with an initial population. Population individuals are of two types: particles and hyper-sphere centers that all together form particle sets. Searching the hyper-sphere inner space made by the hyper-sphere center and its particle is the basis of the proposed evolutionary algorithm. The HSS algorithm hopefully converges to a state at which there exists only one hyper-sphere center, and its particles are at the same position and have the same cost function value as the hyper-sphere center. Applying the proposed algorithm to some benchmark cost functions shows its ability in dealing with different types of optimization problems. The proposed method is compared with the genetic algorithm (GA), particle swarm optimization (PSO) and harmony search algorithm (HSA). The results show that the HSS algorithm has faster convergence and results in better solutions than GA, PSO and HSA.

[1]  Ivor W. Tsang,et al.  A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Babak Sohrabi,et al.  A framework for improving e-commerce websites usability using a hybrid genetic algorithm and neural network system , 2011, Neural Computing and Applications.

[3]  Xin Yao,et al.  A Memetic Algorithm for Periodic Capacitated Arc Routing Problem , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Gevork B. Gharehpetian,et al.  Incomplete information-based decentralized cooperative control strategy for distributed energy resources of VSI-based microgrids , 2013, Neural Computing and Applications.

[5]  Gevork B. Gharehpetian,et al.  Game-theoretic approach to cooperative control of distributed energy resources in islanded microgrid considering voltage and frequency stability , 2013, Neural Computing and Applications.

[6]  Hitoshi Iba,et al.  The Memetic Tree-based Genetic Algorithm and its application to Portfolio Optimization , 2009, Memetic Comput..

[7]  Vassilis P. Plagianakos,et al.  Multi-optima search using Differential Evolution and unsupervised clustering , 2013, 2013 IEEE Congress on Evolutionary Computation.

[8]  Gianluca Rapone,et al.  Optimisation of curtain wall façades for office buildings by means of PSO algorithm , 2012 .

[9]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[10]  Mohammad Hossien Ahmadi,et al.  Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization , 2012, Neural Computing and Applications.

[11]  Enrique Alba,et al.  A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling , 2012, Appl. Soft Comput..

[12]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .

[13]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[14]  Milad Asgarpour Khansary,et al.  Using genetic algorithm (GA) and particle swarm optimization (PSO) methods for determination of interaction parameters in multicomponent systems of liquid–liquid equilibria , 2014 .

[15]  Michal Pavlech.  Self-organizing Migration Algorithm on GPU with CUDA , 2012, SOCO.

[16]  Alex S. Fukunaga,et al.  Distributed island-model genetic algorithms using heterogeneous parameter settings , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[17]  Sanyang Liu,et al.  A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning , 2013, IEEE Transactions on Cybernetics.

[18]  Ray J. Paul,et al.  Optimising a complex discrete event simulation model using a genetic algorithm , 2005, Neural Computing & Applications.

[19]  Antero Arkkio,et al.  A hybrid PBIL-based harmony search method , 2011, Neural Computing and Applications.

[20]  P. Hosseinzadeh Talaee,et al.  Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran , 2012, Neural Computing and Applications.

[21]  Reza Tavakkoli-Moghaddam,et al.  A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects , 2013 .

[22]  Gevork B. Gharehpetian,et al.  Optimal Scheduling of Residential Energy System Including Combined Heat and Power System and Storage Device , 2013 .

[23]  Yaonan Wang,et al.  Harmony search algorithm-based fuzzy-PID controller for electronic throttle valve , 2013, Neural Computing and Applications.

[24]  Mansour Sheikhan,et al.  Substitution of G.728 vocoder’s codebook search module with SOM array trained by PSO-optimized supervised algorithm , 2012, Neural Computing and Applications.

[25]  Vishal Kumar,et al.  Optimal placement of different type of DG sources in distribution networks , 2013 .