Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.

[1]  Yuriy S. Shmaliy,et al.  Self-Tuning Unbiased Finite Impulse Response Filtering Algorithm for Processes With Unknown Measurement Noise Covariance , 2021, IEEE Transactions on Control Systems Technology.

[2]  Amir H. Gandomi,et al.  Marine Predators Algorithm: A nature-inspired metaheuristic , 2020, Expert Syst. Appl..

[3]  Yuriy S. Shmaliy,et al.  Unbiased FIR Filtering for Time-Stamped Discretely Delayed and Missing Data , 2020, IEEE Transactions on Automatic Control.

[4]  A. L. Sangal,et al.  Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization , 2020, Eng. Appl. Artif. Intell..

[5]  Bakir Lacevic,et al.  Wingsuit Flying Search—A Novel Global Optimization Algorithm , 2020, IEEE Access.

[6]  Ying Li,et al.  A Novel Path Planning Algorithm Based on Q-learning and Adaptive Exploration Strategy , 2019, 2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019).

[7]  Bilal Alatas,et al.  Sports inspired computational intelligence algorithms for global optimization , 2019, Artificial Intelligence Review.

[8]  Hossam Faris,et al.  Harris hawks optimization: Algorithm and applications , 2019, Future Gener. Comput. Syst..

[9]  Nor Hidayati Abdul Aziz,et al.  Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer , 2019, MEKATRONIKA.

[10]  Nurettin Cetinkaya,et al.  A new meta-heuristic optimizer: Pathfinder algorithm , 2019, Appl. Soft Comput..

[11]  Nor Hidayati Abdul Aziz,et al.  Evaluation of Different Horizon Lengths in Single-agent Finite Impulse Response Optimizer , 2019, 2019 International Conference on Computer and Information Sciences (ICCIS).

[12]  S. Shadravan,et al.  The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems , 2019, Eng. Appl. Artif. Intell..

[13]  Jaza Mahmood Abdullah,et al.  Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process , 2019, IEEE Access.

[14]  Sadoullah Ebrahimnejad,et al.  Emperor Penguins Colony: a new metaheuristic algorithm for optimization , 2019, Evolutionary Intelligence.

[15]  Vijander Singh,et al.  A novel nature-inspired algorithm for optimization: Squirrel search algorithm , 2019, Swarm Evol. Comput..

[16]  Alper Hamzadayi,et al.  Single Seekers Society (SSS): Bringing together heuristic optimization algorithms for solving complex problems , 2019, Knowl. Based Syst..

[17]  Piotr Kacejko,et al.  A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG) , 2019, Engineering Optimization.

[18]  Abd Aziz Nor Hidayati,et al.  A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO , 2018 .

[19]  Vijay Kumar,et al.  Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..

[20]  Liang Gao,et al.  Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems , 2018, Applied Mathematical Modelling.

[21]  Farhad Soleimanian Gharehchopogh,et al.  Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems , 2018, Appl. Soft Comput..

[22]  Yuriy S. Shmaliy,et al.  ECG Signals Denoising in State Space using UFIR Filtering for Features Extraction , 2018, 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[23]  Andrew Lewis,et al.  Biology migration algorithm: a new nature-inspired heuristic methodology for global optimization , 2018, Soft Comput..

[24]  Nikos D. Lagaros,et al.  Pity beetle algorithm - A new metaheuristic inspired by the behavior of bark beetles , 2018, Adv. Eng. Softw..

[25]  Nor Hidayati Abdul Aziz,et al.  Single-solution Simulated Kalman Filter algorithm for global optimisation problems , 2018, Sādhanā.

[26]  Mohammad Mahdi Paydar,et al.  Tree Growth Algorithm (TGA): A novel approach for solving optimization problems , 2018, Eng. Appl. Artif. Intell..

[27]  Reza Tavakkoli-Moghaddam,et al.  The Social Engineering Optimizer (SEO) , 2018, Eng. Appl. Artif. Intell..

[28]  Yuriy S. Shmaliy,et al.  UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data , 2018, J. Electr. Comput. Eng..

[29]  Yuriy S. Shmaliy,et al.  A Revisit to Strictly Passive FIR Filtering , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[30]  Reza Moghdani,et al.  Volleyball Premier League Algorithm , 2018, Appl. Soft Comput..

[31]  S Mandal,et al.  Elephant swarm water search algorithm for global optimization , 2018 .

[32]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[33]  Gaurav Dhiman,et al.  Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications , 2017, Adv. Eng. Softw..

[34]  Mostafa Meshkat,et al.  Sine Optimization Algorithm (SOA): A novel optimization algorithm by change update position strategy of search agent in Sine Cosine Algorithm , 2017, 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS).

[35]  Yuriy S. Shmaliy,et al.  Unbiased FIR denoising of ECG data for features extraction , 2017, 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).

[36]  Yuriy S. Shmaliy,et al.  Design of an unbiased finite impulse response filter for a smart sensor to estimate state of CO concentration , 2017, 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).

[37]  Yunlong Zhu,et al.  A new meta-heuristic butterfly-inspired algorithm , 2017, J. Comput. Sci..

[38]  Erik Valdemar Cuevas Jiménez,et al.  A global optimization algorithm inspired in the behavior of selfish herds , 2017, Biosyst..

[39]  S. J. Mousavirad,et al.  Human mental search: a new population-based metaheuristic optimization algorithm , 2017, Applied Intelligence.

[40]  Choon Ki Ahn,et al.  Iterative Filter with Finite Measurements for Suddenly Maneuvering Targets , 2017 .

[41]  Arshad Ahmad,et al.  A new optimization method: Electro-Search algorithm , 2017, Comput. Chem. Eng..

[42]  M. Bakhshipour,et al.  Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach , 2017, Appl. Soft Comput..

[43]  A. Kaveh,et al.  A novel meta-heuristic optimization algorithm: Thermal exchange optimization , 2017, Adv. Eng. Softw..

[44]  Gilberto Reynoso-Meza,et al.  Heuristic Kalman Algorithm for Multiobjective Optimization. , 2017 .

[45]  Yuriy S. Shmaliy,et al.  General Unbiased FIR Filter With Applications to GPS-Based Steering of Oscillator Frequency , 2017, IEEE Transactions on Control Systems Technology.

[46]  Bilal Alatas,et al.  Plant intelligence based metaheuristic optimization algorithms , 2017, Artificial Intelligence Review.

[47]  Yuriy S. Shmaliy,et al.  Blind Robust Estimation With Missing Data for Smart Sensors Using UFIR Filtering , 2017, IEEE Sensors Journal.

[48]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[49]  Ali Kaveh,et al.  A NOVEL META-HEURISTIC ALGORITHM: TUG OF WAR OPTIMIZATION , 2016 .

[50]  Yuriy S. Shmaliy,et al.  Ultimate iterative UFIR filtering algorithm , 2016 .

[51]  Aboelsood Zidan,et al.  A new rooted tree optimization algorithm for economic dispatch with valve-point effect , 2016 .

[52]  Fei Liu,et al.  Fast Kalman-Like Optimal Unbiased FIR Filtering With Applications , 2016, IEEE Transactions on Signal Processing.

[53]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[54]  Fei Liu,et al.  Unbiased, optimal, and in-betweens: the trade-off in discrete finite impulse response filtering , 2016, IET Signal Process..

[55]  Marizan Mubin,et al.  Statistical Analysis for Swarm Intelligence Simplified , 2015 .

[56]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[57]  Seyed Mohammad Mirjalili,et al.  Ions motion algorithm for solving optimization problems , 2015, Appl. Soft Comput..

[58]  Fei Liu,et al.  Fast Computation of Discrete Optimal FIR Estimates in White Gaussian Noise , 2015, IEEE Signal Processing Letters.

[59]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[60]  Tamer Ölmez,et al.  A new metaheuristic for numerical function optimization: Vortex Search algorithm , 2015, Inf. Sci..

[61]  Alireza Askarzadeh,et al.  Bird mating optimizer: An optimization algorithm inspired by bird mating strategies , 2014, Commun. Nonlinear Sci. Numer. Simul..

[62]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[63]  Choon Ki Ahn,et al.  A new solution to the induced l∞ finite impulse response filtering problem based on two matrix inequalities , 2014, Int. J. Control.

[64]  Z. Beheshti.  A review of population-based meta-heuristic algorithm , 2013, SOCO 2013.

[65]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[66]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[67]  Dan Simon,et al.  Iterative unbiased FIR state estimation: a review of algorithms , 2013, EURASIP J. Adv. Signal Process..

[68]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[69]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[70]  E. Talbi,et al.  Metaheuristics: From Design to Implementation , 2009 .

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

[72]  Mitsuo Gen,et al.  Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation , 2008, Soft Comput..

[73]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[74]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[75]  Wook Hyun Kwon,et al.  A receding horizon unbiased FIR filter for discrete-time state space models , 2002, Autom..

[76]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[77]  Jay H. Lee,et al.  Receding Horizon Recursive State Estimation , 1993, 1993 American Control Conference.

[78]  Liying Wang,et al.  Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications , 2020, Eng. Appl. Artif. Intell..

[79]  Vahideh Hayyolalam,et al.  Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems , 2020, Eng. Appl. Artif. Intell..

[80]  Mohamed Othman,et al.  Raccoon Optimization Algorithm , 2019, IEEE Access.

[81]  Shunyi Zhao,et al.  Single-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems , 2018, IEEE Access.

[82]  Yuriy S. Shmaliy,et al.  New Receding Horizon FIR Estimator for Blind Smart Sensing of Velocity via Position Measurements , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[83]  Kourosh Eshghi,et al.  A Metaheuristic Algorithm Based on Chemotherapy Science: CSA , 2017 .

[84]  Salwani Abdullah,et al.  Kidney-inspired algorithm for optimization problems , 2017, Commun. Nonlinear Sci. Numer. Simul..

[85]  Lei Zhang,et al.  A novel path planning algorithm based on plant growth mechanism , 2017, Soft Comput..

[86]  F. Merrikh Bayat,et al.  The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature , 2015, Appl. Soft Comput..

[87]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[88]  Thomas Stützle,et al.  Classification of Metaheuristics and Design of Experiments for the Analysis of Components , 2001 .