The improvements took place in the design optimization techniques in recent years have helped engineers tremendously in finding the solutions of computationally intractable design problems. The fundamental properties of metaheuristic algorithms are that they imitate certain strategies taken from nature, social culture, biology or laws of physics that guide the search process. Their goal is to efficiently explore the search space using these guiding mechanisms in order to find near optimal solutions. They also use 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. The mechanisms used in search of the optimum solution are not deterministic but stochastic. This why they are approximate techniques and there is no mathematical proof that the optimum solution determined is the global optimum. However they are not problem specific and proven to be very efficient and robust in obtaining the solution of practical engineering design optimization problems with both continuous and discrete design variables. The mechanisms employed in search of optimum solution in these techniques simulates the 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. A lot of new algorithms are added to metaheuristic techniques in recent years. The aim of this session is to generate an atmosphere where these new techniques are introduced and their application in engineering design optimization are presented. Hence talks related with new metaheuristic algorithms such as:
- harmony search
- big bang-big crunch
- artificial bee colony
- scattered search
- hunting search
- particle swarm optimizer
- Intelligent group search
- Cuckoo search
- Differential evolution