Recent Developments in Metaheuristic Algorithms a Review Saka 2012

Topics: Optimization, Particle swarm optimization, Optimization algorithms Pages: 55 (15059 words) Published: June 27, 2012

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


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 imitates the breeding behaviour of certain cuckoo species. Hunting search algorithm is inspired by group of hunting animals such as lions, wolves, and dolphins. Charged system search utilizes Coulomb law from electrostatics and Newtonian laws of mechanics. This paper reviews those of nature inspired metaheuristic techniques which emerged and published in the literature after 1995. Keywords: Metaheuristic algorithms, stochastic search techniques, combinatorial optimization, swarm intelligence based algorithms, harmony search method.




Stochastic search algorithms have attracted a lot of attention in recent years due to their robust and efficient performance in solving obstinate engineering design optimization problems compare to deterministic algorithms. These methods move within a design domain randomly with the objective of reaching the optimum solution. However, this random move is not based on a blind way of searching for the optimum in a confined design region but it makes use of an intelligent heuristics to guide the search. This is why stochastic search methods are also called metaheuristic algorithms. The fundamental properties of metaheuristic algorithms are that they imitate certain strategies taken from nature, social culture, biology or laws of physics that direct the search process. Their goal is to efficiently explore the search space using these governing mechanisms in order to find near optimal solutions if not global optimum. They also utilize some strategies to avoid getting trapped in confined areas of search space. Furthermore they do not even require an explicit relationship between the objective function and the constraints. Metaheuristic techniques are approximate techniques and there is no mathematical proof that the optimum solution obtained is the global one. However they are not problem specific and proven to be very efficient and robust in obtaining the solution of practical engineering design optimization...
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