Supply-Demand-Based Optimization: A Novel Economics-Inspired Algorithm for Global Optimization

A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/71764-supply-demand-based-optimization.

[1]  Hae Chang Gea,et al.  STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD , 1996 .

[2]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[3]  Zhenxing Zhang,et al.  A novel atom search optimization for dispersion coefficient estimation in groundwater , 2019, Future Gener. Comput. Syst..

[4]  Ling Wang,et al.  A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization , 2007, Appl. Math. Comput..

[5]  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 .

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

[7]  Liying Wang,et al.  A bare bones bacterial foraging optimization algorithm , 2018, Cognitive Systems Research.

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

[9]  H Nowacki,et al.  OPTIMIZATION IN PRE-CONTRACT SHIP DESIGN , 1973 .

[10]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[11]  Hamed Shah-Hosseini,et al.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011, Int. J. Comput. Sci. Eng..

[12]  F. N. Hassan,et al.  Optimal PID control of a brushless DC motor using PSO and BF techniques , 2014 .

[13]  Hui Zhao,et al.  A novel nature-inspired algorithm for optimization: Virus colony search , 2016, Adv. Eng. Softw..

[14]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[15]  Ajith Abraham,et al.  Ideology algorithm: a socio-inspired optimization methodology , 2017, Neural Computing and Applications.

[16]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[17]  Douglas H. Werner,et al.  The Wind Driven Optimization Technique and its Application in Electromagnetics , 2013, IEEE Transactions on Antennas and Propagation.

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

[19]  Michael N. Vrahatis,et al.  Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems , 2005, ICNC.

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

[21]  C. Coello.  TREATING CONSTRAINTS AS OBJECTIVES FOR SINGLE-OBJECTIVE EVOLUTIONARY OPTIMIZATION , 2000 .

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

[23]  Zhihua Cui,et al.  Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems , 2010, SEMCCO.

[24]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[25]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

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

[27]  Giovanni Squillero,et al.  A new evolutionary algorithm inspired by the selfish gene theory , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[28]  Liying Wang,et al.  An effective bacterial foraging optimizer for global optimization , 2016, Inf. Sci..

[29]  Mordecai Ezekiel,et al.  The Cobweb Theorem , 2010 .

[30]  Kalyanmoy Deb,et al.  Optimizing Engineering Designs Using a Combined Genetic Search , 1997, ICGA.

[31]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[32]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

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

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

[35]  Rajiv Tiwari,et al.  Optimum design of rolling element bearings using genetic algorithms , 2007 .

[36]  Che-Lun Hung,et al.  Parallel genetic-based algorithm on multiple embedded graphic processing units for brain magnetic resonance imaging segmentation , 2017, Comput. Electr. Eng..

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

[38]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[39]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[40]  Anupam Yadav,et al.  Gravitational Search Algorithm: A State-of-the-Art Review , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[41]  Bing Yang,et al.  Flood risk zoning using a rule mining based on ant colony algorithm , 2016 .

[42]  Dayang N. A. Jawawi,et al.  Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm , 2016, Swarm Evol. Comput..

[43]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[44]  Inderveer Chana,et al.  Bacterial foraging based hyper-heuristic for resource scheduling in grid computing , 2013, Future Gener. Comput. Syst..

[45]  M. J. Mahjoob,et al.  A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search , 2010, Comput. Math. Appl..

[46]  Ardeshir Bahreininejad,et al.  Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems , 2013, Appl. Soft Comput..

[47]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[48]  Heinz Mühlenbein,et al.  Evolution algorithms in combinatorial optimization , 1988, Parallel Comput..

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

[50]  Wenjian Luo,et al.  Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..

[51]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[52]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[53]  Anand Jayant Kulkarni,et al.  Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology , 2018, Future Gener. Comput. Syst..

[54]  Konstantinos G. Margaritis,et al.  On benchmarking functions for genetic algorithms , 2001, Int. J. Comput. Math..

[55]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

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

[57]  Carlos A. Coello Coello,et al.  Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms , 2005, MICAI.

[58]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[59]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[60]  Rajiv Tiwari,et al.  Multi-objective design optimisation of rolling bearings using genetic algorithms , 2007 .

[61]  Zhun Fan,et al.  Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique , 2009 .

[62]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[63]  Ricardo Landa Becerra,et al.  Efficient evolutionary optimization through the use of a cultural algorithm , 2004 .

[64]  Ali Kaveh,et al.  Water Evaporation Optimization , 2016 .

[65]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[66]  Ali Kaveh,et al.  Colliding Bodies Optimization method for optimum discrete design of truss structures , 2014 .

[67]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[68]  Ibrahim Eksin,et al.  Big Bang - Big Crunch optimization algorithm hybridized with local directional moves and application to target motion analysis problem , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[69]  D. P. Tripathi,et al.  Cognitive and social information based PSO , 2016 .

[70]  C. H. Lin,et al.  Cultural Evolution Algorithm for Global Optimizations and its Applications , 2013 .

[71]  A. Goldstein.  On Steepest Descent , 1965 .

[72]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[73]  James N. Siddall,et al.  Optimal Engineering Design: Principles and Applications , 1982 .

[74]  A. Kaveh,et al.  A new optimization method: Dolphin echolocation , 2013, Adv. Eng. Softw..

[75]  Salwani Abdullah,et al.  Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting , 2018, Eng. Appl. Artif. Intell..

[76]  Jonas Krause,et al.  A Survey of Swarm Algorithms Applied to Discrete Optimization Problems , 2013 .

[77]  Mustafa Servet Kiran,et al.  TSA: Tree-seed algorithm for continuous optimization , 2015, Expert Syst. Appl..

[78]  Oscar Castillo,et al.  Human evolutionary model: A new approach to optimization , 2007, Inf. Sci..

[79]  Ling Wang,et al.  An effective co-evolutionary differential evolution for constrained optimization , 2007, Appl. Math. Comput..

[80]  Sunethra Weerakoon,et al.  A variant of Newton's method with accelerated third-order convergence , 2000, Appl. Math. Lett..

[81]  Raghunathan Rengaswamy,et al.  A Genetic Algorithm (GA) based rational approach for design of discrete microfluidic networks , 2012 .

[82]  Ali Husseinzadeh Kashan,et al.  League Championship Algorithm: A New Algorithm for Numerical Function Optimization , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[83]  Suresh Chandra Satapathy,et al.  Social group optimization (SGO): a new population evolutionary optimization technique , 2016 .

[84]  Richard A. Formato,et al.  CENTRAL FORCE OPTIMIZATION: A NEW META-HEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS , 2007 .

[85]  J. Meza,et al.  Steepest descent , 2010 .

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

[87]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[88]  Jing J. Liang,et al.  Novel composition test functions for numerical global optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..