Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms and gradient search: similarities anddifferences
IEEE Transactions on Evolutionary Computation
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Big Bang Big Crunch Optimization Method Based Fuzzy Model Inversion
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Inverse modeling in geoenvironmental engineering using a novel particle swarm optimization algorithm
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Structural and Multidisciplinary Optimization
Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm
Expert Systems with Applications: An International Journal
ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization
Expert Systems with Applications: An International Journal
A stochastic neighborhood search approach for airport gate assignment problem
Expert Systems with Applications: An International Journal
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Computers and Structures
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Upper bound strategy for metaheuristic based design optimization of steel frames
Advances in Engineering Software
Sizing truss structures using teaching-learning-based optimization
Computers and Structures
A new optimization method: Dolphin echolocation
Advances in Engineering Software
Structural and Multidisciplinary Optimization
Interval type-2 fuzzy PID load frequency controller using Big Bang-Big Crunch optimization
Applied Soft Computing
Neurocomputing
Advances in Engineering Software
Advances in Engineering Software
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Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang-Big Crunch (BB-BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB-BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions.