Brief History and Overview of Intelligent Optimization Algorithms

Up to now, intelligent optimization algorithm has been developed for nearly 40 years. It is one of the main research directions in the field of algorithm and artificial intelligence. No matter for complex continuous problems or discrete NP-hard combinatorial optimizations, people nowadays is more likely to find a feasible solution by using such randomized iterative algorithm within a short period of time instead of traditional deterministic algorithms. In this chapter, the basic principle of algorithms, research classifications, and the development trends of intelligent optimization algorithm are elaborated.

[1]  Richard M. Karp,et al.  Combinatorics, complexity, and randomness , 1986, CACM.

[2]  Bassem Jarboui,et al.  Genetic algorithm with iterated local search for solving a location-routing problem , 2012, Expert Syst. Appl..

[3]  Victor O. K. Li,et al.  Chemical-Reaction-Inspired Metaheuristic for Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[4]  William J. Cook,et al.  Combinatorial optimization , 1997 .

[5]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..

[6]  Jean Charles Gilbert,et al.  Numerical Optimization: Theoretical and Practical Aspects , 2003 .

[7]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[8]  Sabre Kais,et al.  Group leaders optimization algorithm , 2010, ArXiv.

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

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

[12]  Andrew Taylor,et al.  Modeling and Control of a Plastic Film Manufacturing Web Process , 2011, IEEE Transactions on Industrial Informatics.

[13]  J. Renaud Numerical Optimization, Theoretical and Practical Aspects— , 2006, IEEE Transactions on Automatic Control.

[14]  Michael Defoin-Platel,et al.  Quantum-Inspired Evolutionary Algorithm: A Multimodel EDA , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Yoshikazu Fukuyama,et al.  Parallel genetic algorithm for generation expansion planning , 1996 .

[16]  Ioannis G. Tsoulos,et al.  Modifications of real code genetic algorithm for global optimization , 2008, Appl. Math. Comput..

[17]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[18]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[19]  Hong-Tzer Yang,et al.  A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks , 1997 .

[20]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[21]  Mauro Birattari,et al.  On the Invariance of Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[22]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[23]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[24]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[25]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[26]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

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

[28]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

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

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

[31]  M. Montaz Ali,et al.  A direct search variant of the simulated annealing algorithm for optimization involving continuous variables , 2002, Comput. Oper. Res..

[32]  Rubén Ruiz,et al.  Iterated greedy local search methods for unrelated parallel machine scheduling , 2010, Eur. J. Oper. Res..

[33]  Andrew Y. C. Nee,et al.  A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise , 2010 .

[34]  D. Xu,et al.  Design of optimal digital filter using a parallel genetic algorithm , 1995 .

[35]  J. March Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.

[36]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[37]  Gexiang Zhang,et al.  Quantum-inspired evolutionary algorithms: a survey and empirical study , 2011, J. Heuristics.

[38]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[39]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[40]  Chun Wang,et al.  Optimization of Network Configuration in Large Distribution Systems Using Plant Growth Simulation Algorithm , 2008, IEEE Transactions on Power Systems.

[41]  Sou-Sen Leu,et al.  Metaheuristics for project and construction management – A state-of-the-art review , 2011 .

[42]  Fei Tao,et al.  Resource Service Composition and Its Optimal-Selection Based on Particle Swarm Optimization in Manufacturing Grid System , 2008, IEEE Transactions on Industrial Informatics.

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

[44]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[45]  Alexandre Linhares State-space search strategies gleaned from animal behavior: a traveling salesman experiment , 1998, Biological Cybernetics.

[46]  Lothar M. Schmitt,et al.  Theory of genetic algorithms , 2001, Theor. Comput. Sci..

[47]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[48]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[49]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[50]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[51]  Kim-Fung Man,et al.  A Theoretical Development and Analysis of Jumping Gene Genetic Algorithm , 2011, IEEE Transactions on Industrial Informatics.

[52]  Jiao Li-cheng The Immune Algorithm , 2000 .

[53]  Stanislaw Gawiejnowicz,et al.  Time-Dependent Scheduling , 2008, Monographs in Theoretical Computer Science. An EATCS Series.

[54]  Kenji Onaga,et al.  A Parallel and Distributed Genetic Algorithm on Loosely-Coupled Multiprocessor Systems(Special Section on Concurrent Systems Technology) , 1998 .

[55]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[56]  Yaghout Nourani,et al.  A comparison of simulated annealing cooling strategies , 1998 .

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

[58]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[59]  Jeffrey D. Ullman,et al.  NP-Complete Scheduling Problems , 1975, J. Comput. Syst. Sci..

[60]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

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

[62]  Dingwei Wang,et al.  A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[63]  W. Lei The Immune Programming , 2000 .

[64]  James D. Cohoow,et al.  On the acceleration of simulated annealing , 1996 .

[65]  Liang Gao,et al.  An effective genetic algorithm for the flexible job-shop scheduling problem , 2011, Expert Syst. Appl..

[66]  Monique Snoeck,et al.  Classification With Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[67]  Kim-Fung Man,et al.  Computational Optimization Algorithms for Antennas and RF/Microwave Circuit Designs: An Overview , 2012, IEEE Transactions on Industrial Informatics.

[68]  Celso C. Ribeiro,et al.  Metaheuristics for optimization problems in computer communications , 2007, Comput. Commun..

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

[70]  T. Stützle,et al.  Iterated Local Search: Framework and Applications , 2018, Handbook of Metaheuristics.

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

[72]  E. H. K. Fung,et al.  Intelligent Automatic Fault Detection for Actuator Failures in Aircraft , 2009, IEEE Transactions on Industrial Informatics.

[73]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..