Recent Developments in Metaheuristic Algorithms a Review Saka 2012
RECENT DEVELOPMENTS IN METAHEURISTIC ALGORITHMS: A Review M. P. Saka*
*
E. Doğan‡
Corresponding author, Prof. Dr., University of Bahrain, Department of Civil Engineering, Isa Town, Bahrain
‡
Assistant Professor, Celal Bayar University, Civil Engineering Department, Manisa, Turkey
Stream: ECT2012RL Reference: ECT2012RL/2011/00005
1
Abstract
Recent developments in optimization techniques that deals in finding the solution of combinatorial optimization problems has provided engineering designers new capabilities. These new optimization algorithms are called metaheuristic techniques and they use nature as a source of inspiration to develop new numerical optimization procedures. It is shown in the literature that these techniques are robust and efficient and their performance is not affected by the complexity of optimization problems. In last two decades several metaheuristic algorithms are developed that mimic natural phenomena. Among these evolutionary algorithms imitate evolutionary biology and make use of the principle of the survival of the fittest to establish a numerical search algorithm. Swarm intelligence is based on the collective behaviour of insect swarm, bird flocking or fish schooling. Particle swarm optimizer turns this collective behaviour of particles into a numerical optimization algorithm. Differential evolution is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Harmony search method mimics the musical performance process that takes place when a musician searches for a better state of harmony. Big Bang-Big Crunch method simulates the theory of evolution of the universe. Artificial bee colony algorithm is based on the intelligent behaviour of honey bee swarm. Fireflies communicate, search for pray and find mates using bioluminescence with varied flashing patterns. Firefly algorithm mimics the social behaviour of fireflies. Cuckoo search algorithm
*
E. Doğan‡
Corresponding author, Prof. Dr., University of Bahrain, Department of Civil Engineering, Isa Town, Bahrain
‡
Assistant Professor, Celal Bayar University, Civil Engineering Department, Manisa, Turkey
Stream: ECT2012RL Reference: ECT2012RL/2011/00005
1
Abstract
Recent developments in optimization techniques that deals in finding the solution of combinatorial optimization problems has provided engineering designers new capabilities. These new optimization algorithms are called metaheuristic techniques and they use nature as a source of inspiration to develop new numerical optimization procedures. It is shown in the literature that these techniques are robust and efficient and their performance is not affected by the complexity of optimization problems. In last two decades several metaheuristic algorithms are developed that mimic natural phenomena. Among these evolutionary algorithms imitate evolutionary biology and make use of the principle of the survival of the fittest to establish a numerical search algorithm. Swarm intelligence is based on the collective behaviour of insect swarm, bird flocking or fish schooling. Particle swarm optimizer turns this collective behaviour of particles into a numerical optimization algorithm. Differential evolution is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Harmony search method mimics the musical performance process that takes place when a musician searches for a better state of harmony. Big Bang-Big Crunch method simulates the theory of evolution of the universe. Artificial bee colony algorithm is based on the intelligent behaviour of honey bee swarm. Fireflies communicate, search for pray and find mates using bioluminescence with varied flashing patterns. Firefly algorithm mimics the social behaviour of fireflies. Cuckoo search algorithm
References: 42 [10] X-S, Yang, “Engineering Optimization: An Introduction with Metaheuristic Applications”, John Wiley, 2010 47 Algorithms”, Artificial Intelligence Review, Doi: 10.1007/s10462-011-9276-0, 1-32, 2011