In particular, these methods do not care with the information of previous population. Evolutionary based methods start with random population and then evaluate this initial population using one or several operators such as crossover, mutation and selection during the optimization process. The meta-heuristic methods can be taken into account in three classes: evolutionary based , physical-based and swarm intelligence based . According to this ability, the meta-heuristics can be implemented to many research areas. This allows them to be able to avoid local optimums. Also, in contrast of gradient-based methods, meta-heuristics optimize a problem stochastically, say, the process starts with random initial solution or solutions and they improve these solutions using the random operators during the process. So that, these methods become derivative-free. Moreover, these methods are concerned only with the inputs and outputs of a problem. Furthermore, heuristic methods can be modified for different areas without any major modifications in their structures. Also, this helps the researchers to implement easily meta-heuristic methods to their problems.

Generally, these methods have been based on simple concepts of physical phenomena in nature. First of all, it can be mentioned that a heuristic method is simple. This is due to their flexibility, derivation-free mechanisms and local optima avoidance. The swarm based meta-heuristic optimization algorithms have an important role to optimize the modern engineering problems. Furthermore, the method provides very competitive solutions compared to well-known meta-heuristics in literature, such as particle swarm optimization, artificial bee colony, firefly and grey wolf optimizer. The results of final case study verify the superiority of the algorithms proposed in solving challenging real-world problems with unknown search spaces. The proposed PFA and MPFA are implemented to some design problems and a multi-objective engineering problem which is time consuming and computationally expensive. The results obtained show that it can approximate to true Pareto optimal solutions. Also, PFA is designed for multi-objective problems (MOPFA). It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively. The proposed method is tested on some optimization problems to show and confirm the performance on test beds. This method is inspired by collective movement of animal group and mimics the leadership hierarchy of swarms to find best food area or prey. This paper proposes a new meta-heuristic algorithm called Pathfinder Algorithm (PFA) to solve optimization problems with different structure.

Path finder algorithm