Crisscross optimization algorithm and its application

How to improve the global search ability without significantly impairing the convergence speed is still a big challenge for most of the meta-heuristic optimization algorithms. In this paper, a concept for the optimization of continuous nonlinear functions applying crisscross optimization algorithm is introduced. The crisscross optimization algorithm is a new search algorithm inspired by Confucian doctrine of gold mean and the crossover operation in genetic algorithm, which has distinct advantages in solution accuracy as well as convergence rate compared to other complex optimization algorithms. The procedures and related concepts of the proposed algorithm are presented. On this basis, we discuss the behavior of the main search operators such as horizontal crossover and vertical crossover. It is just because of the perfect combination of both, leading to a magical effect on improving the convergence speed and solution accuracy when addressing complex optimization problems. Twelve benchmark functions, including unimodal, multimodal, shifted and rotated functions, are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that the crisscross optimization algorithm has an excellent performance on most of the test functions, compared to other heuristic algorithms. At the end, the crisscross optimization algorithm is successfully applied to the optimization of a large-scale economic dispatch problem in electric power system. It is concluded that the crisscross optimization algorithm is not only robust in solving continuous nonlinear functions, but also suitable for addressing the complex real-world engineering optimization problems.

[1]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[2]  Liang Zhao,et al.  PSO-based single multiplicative neuron model for time series prediction , 2009, Expert Syst. Appl..

[3]  Leandro dos Santos Coelho,et al.  An Efficient Particle Swarm Optimization Approach Based on Cultural Algorithm Applied to Mechanical Design , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Rong-Jong Wai,et al.  Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization , 2011, IEEE Transactions on Industrial Electronics.

[5]  Prabhas Chongstitvatana,et al.  A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification , 2012, Neural Computing and Applications.

[6]  A. Irawan,et al.  A Fast Discrete Gravitational Search Algorithm , 2012, 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation.

[7]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[8]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[9]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Siti Mariyam Hj. Shamsuddin,et al.  CAPSO: Centripetal accelerated particle swarm optimization , 2014, Inf. Sci..

[11]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[12]  N. Amjady,et al.  Nonconvex Economic Dispatch With AC Constraints by a New Real Coded Genetic Algorithm , 2009, IEEE Transactions on Power Systems.

[14]  Sofiene Kachroudi,et al.  Predictive Driving Guidance of Full Electric Vehicles Using Particle Swarm Optimization , 2012, IEEE Transactions on Vehicular Technology.

[15]  Bijaya K. Panigrahi,et al.  Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[17]  M. Pandit,et al.  Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch , 2008, IEEE Transactions on Power Systems.

[18]  C.-L. Chiang,et al.  Genetic-based algorithm for power economic load dispatch , 2007 .

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Q. H. Wu,et al.  A heuristic particle swarm optimizer for optimization of pin connected structures , 2007 .

[21]  Feng Liu,et al.  The Group Search Optimizer and its Application to Truss Structure Design , 2008, 2008 Fourth International Conference on Natural Computation.

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[24]  Jim Euchner.  Design , 2014, Catalysis from A to Z.

[25]  Taher Niknam,et al.  Reserve Constrained Dynamic Economic Dispatch: A New Fast Self-Adaptive Modified Firefly Algorithm , 2012, IEEE Systems Journal.

[26]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[27]  Teresa B Ludermir,et al.  Global Optimization Methods for Designing and Training Feedforward Artificial Neural Networks , 2007 .

[28]  Yanfeng Wang,et al.  A modified invasive weed optimization with crossover operation , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[29]  Li Ying,et al.  A dynamic particle swarm optimizer for educational data processing , 2012, 2012 International Symposium on Information Technologies in Medicine and Education.

[30]  Jong-Bae Park,et al.  An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems , 2010 .

[31]  Ismael Rodríguez,et al.  Using River Formation Dynamics to Design Heuristic Algorithms , 2007, UC.

[32]  Jong-Bae Park,et al.  An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems , 2010, IEEE Transactions on Power Systems.

[33]  Yash P. Aneja,et al.  An ant colony optimization metaheuristic for machine-part cell formation problems , 2010, Comput. Oper. Res..

[34]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[35]  Yangmin Li,et al.  Design, Analysis, and Test of a Novel 2-DOF Nanopositioning System Driven by Dual Mode , 2013, IEEE Transactions on Robotics.

[36]  Swagatam Das,et al.  Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers , 2013, Appl. Soft Comput..

[37]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[38]  Durbadal Mandal,et al.  Optimization of linear phase FIR band pass filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach , 2011, 2011 IEEE Symposium on Industrial Electronics and Applications.

[39]  Mohammed El-Abd,et al.  An improved global-best harmony search algorithm , 2013, Appl. Math. Comput..

[40]  Wei-Chang Yeh,et al.  A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method , 2009, Expert Syst. Appl..

[41]  Hao Yin,et al.  Multi-agent Based Distributed Genetic Algorithm Applied to the Optimization of Self-Adaptive PID Parameters of Hydro-turbine , 2012 .

[42]  Tai-Hsi Wu,et al.  A simulated annealing algorithm for manufacturing cell formation problems , 2008, Expert Syst. Appl..

[43]  Siavash Khorsandi,et al.  Self-Organized Cooperation Policy Setting in P2P Systems Based on Reinforcement Learning , 2013, IEEE Systems Journal.

[44]  E. Kyriakides,et al.  A GA-API Solution for the Economic Dispatch of Generation in Power System Operation , 2012, IEEE Transactions on Power Systems.

[45]  Osvaldo R. Saavedra,et al.  EFFICIENT EVOLUTIONARY STRATEGY OPTIMISATION PROCEDURE TO SOLVE THE NONCONVEX ECONOMIC DISPATCH PROBLEM WITH GENERATOR CONSTRAINTS , 2005 .

[46]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[47]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[48]  C. Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[49]  Yuanxin Wu,et al.  Calibration of Three-Axis Magnetometer Using Stretching Particle Swarm Optimization Algorithm , 2013, IEEE Transactions on Instrumentation and Measurement.

[50]  Yuwen Zhang,et al.  Combinatorial optimization using FOA and GA in futures market technical analysis , 2012, 2012 12th International Conference on Control, Automation and Systems.

[51]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[52]  Adel M. Alimi,et al.  Hybrid harmony search algorithm for global optimization , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.

[53]  Bhawna Tandon Asstt.  GENETIC ALGORITHM BASED PARAMETER TUNING OF PID CONTROLLER FOR COMPOSITION CONTROL SYSTEM , 2011 .

[54]  Archana Sarangi,et al.  DEPSO and PSO-QI in digital filter design , 2011, Expert Syst. Appl..

[55]  Xiaoli Li,et al.  Application of a group search optimization based Artificial Neural Network to machine condition monitoring , 2008, 2008 IEEE International Conference on Emerging Technologies and Factory Automation.

[56]  Q. Henry Wu,et al.  A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[57]  Wei-Chang Yeh,et al.  New Parameter-Free Simplified Swarm Optimization for Artificial Neural Network Training and its Application in the Prediction of Time Series , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[58]  Hamed Shah-Hosseini,et al.  Problem solving by intelligent water drops , 2007, 2007 IEEE Congress on Evolutionary Computation.

[59]  Teresa Bernarda Ludermir,et al.  Cooperative Group Search Optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[60]  Sakti Prasad Ghoshal,et al.  Digital stable IIR low pass filter optimization using PSO-CFIWA , 2012, 2012 1st International Conference on Recent Advances in Information Technology (RAIT).