Keywords: metaheuristic algorithms, stochastic search techniques, combinatorial optimisation, swarm intelligence based algorithms, harmony search method.

Stochastic search algorithms have attracted a lot of attention in recent years as a result of their robust and efficient performance in solving obstinate engineering design optimisation problems compared 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. The mechanisms employed in search of an optimum solution in these techniques simulates natural phenomena such as survival of the fittest, immune system, swarm intelligence, the cooling process of molten metals through annealing, social culture, music improvisation, big bang- big crunch theory, into a numerical algorithm.

The search to find computationally more efficient metaheuristic algorithms has gained pace since 2000. Some of the new techniques that emerged after 2000 are the harmony search method, big bang-big crunch algorithm, artificial bee colony algorithm, firefly algorithm, hunting search algorithm, group search algorithm, cuckoo search algorithm, and charged system search algorithm. Harmony search method is a numerical optimisation method that imitates the musical performance process which takes place when a musician searches for a better state of harmony. Big bang-big crunch algorithm is based on the theories of evolution of the universe, namely the big bang and big crunch theory. Artificial Bee colony algorithm is based on the intelligent behavior of honey bee swarm. Firefly algorithm makes use of the idealised behavior of the flashing characteristic of fireflies. Hunting search algorithm imitates hunting strategies of animals such as lions, wolves, and dolphins. Group search algorithm adopts the scrounging strategy of house sparrows and employs special animal scanning mechanisms to perform searching process. Cuckoo search algorithm is based on the breeding strategy of some cuckoo birds species. Charged system search utilises the governing Coulomb law from electrostatics and the Newtonian laws of mechanics. Each charged particle can affect others based on its fitness values and their distance from others. Performance comparison of these new techniques with the early metaheuristic algorithms has shown that some of them can even outperform genetic algorithms. The aim of this special lecture is to introduce these recent metaheuristic techniques and review their application in engineering design optimisation.

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