Survey of Robot 3D Path Planning Algorithms <s> Evolutionary Algorithm. <s> We introduce the Vibration concept for real coded Genetic Algorithm and its implementation to inverse airfoil design, which decreases the number of CFD calculations. This concept assures efficient diversity in the population and consequently gives faster solution. We used the Vibration concept as vibrational mutation and vibrational crossover. For the mutational manner, a sinusoidal wave with random amplitude is introduced into population periodically from the beginning of the initial step of the genetic process. This operation spreads out the population over the design space and increases exploration performance of the genetic process. This makes passing over local optimums for genetic algorithm easy. In order to apply this new concept at the crossover stages, Double Directional Alpha (DDA) approximation in BLX‐α as a new crossover technique which was already presented in our earlier study is used. In the developed technique, the value of α oscillates systematically during the genetic process. Implementation of the Vibration concept to the inverse airfoil design makes the convergence faster. <s> BIB001 </s> Survey of Robot 3D Path Planning Algorithms <s> Evolutionary Algorithm. <s> In this study, the power of vibrational genetic algorithm (VGA) for transonic airfoil design and optimisation problems, which are generally characterized by multi‐model topology in the design parameter space, has been introduced. This type of problem is characterized by search and computational time to achieve satisfying solutions. In order to obtain more robust and faster algorithm, vibration concept, our earlier study, is further developed. This developed VGA is coupled with a full potential flow‐field solver for inverse design and airfoil optimisation problem in transonic case. The performance of this implemented strategy is compared with that offered by a classical or more commonly used genetic algorithm. <s> BIB002 </s> Survey of Robot 3D Path Planning Algorithms <s> Evolutionary Algorithm. <s> The paper presents a genetic algorithm to find and optimize solutions for nonholonomic motion planning problems. Mainly focusing on mobile robots, the algorithm uses present randomized algorithms to come up with suboptimal paths and iteratively optimizes them according to a fitness function which includes domain specific knowledge. The major advantages of this method include being an any-time algorithm, and improving the quality of the solution throughout the evolutionary process. An extensive experimental analysis comparing our results with state of the art algorithms outline the effectiveness of the proposed methodology. <s> BIB003 </s> Survey of Robot 3D Path Planning Algorithms <s> Evolutionary Algorithm. <s> Abstract A new optimization algorithm called multi-frequency vibrational genetic algorithm (mVGA) that can be used to solve the path planning problems of autonomous unmanned aerial vehicles (UAVs) is significantly improved. The algorithm emphasizes a new mutation application strategy and diversity variety such as the global random and the local random diversity. Clustering method and Voronoi diagram concepts are used within the initial population phase of mVGA process. The new algorithm and three additional GAs in the literature are applied to the path planning problem in two different three-dimensional (3D) environments such as sinusoidal and city type terrain models, and their results are compared. For both of the demonstration problems considered, remarkable reductions in the computational times have been accomplished. <s> BIB004
Evolutionary Algorithm is an umbrella name which includes Genetic Algorithm (GA), Memetic Algorithm (MA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Shuffled Frog Leaping Algorithm (SFLA). EA was propounded to solve the problem where traditional linear programming and dynamic programming often fail to solve NP-hard problems with large number of variables. EA is a stochastic search approach that imitates the metaphor of natural biological evolution and social behavior. The firstly proposed and now widely implemented Evolutionary method is GA; then inspired by different natural process four other methods developed. Figure 12 illustrates a typical flowchart of Evolutionary Algorithm, which is proposed by Dong and Juris  . Evolutionary Algorithms start by selecting randomly feasible solutions as the first generation. Then it takes the environment, robot's capacity (dynamic ability), goal, and other constraints into consideration, to evaluate the fitness of each individual. In the next step, a set of individuals is selected as parents for the next generation according to their fitness. The last step is a mutation and crossover step. The whole process is performed in an iterative way and stops the process when a preset goal is achieved. The best fitness individuals are decoded as the optimal path nodes. 7.1.1. Genetic Algorithms. Holland  firstly introduced GA, and now it is the most popular population-based optimization method. The basic version of GA defines a cost function to evaluate the potential solutions. Then a partly random crossover operator takes two parents from the population set and recombines them in some way. The mutation operator tries to modify the solutions and aims to achieve a valid solution in order to escape local optimality. GA holds the process that all individuals can exchange information; by doing this way, this nest generation can converge very fast by using this information. However, if the population becomes too similar and loses population diversity, it often leads to premature convergence. If there exists too much population diversity, it may result in a heavy computational burden to investigate the poor solutions. GA needs to repeatedly evaluate the fitness of the current generation, which also causes a high computational burden. Fonlupt et al.  proposed a cooperating GA to solve the premature convergence problem of typical GA. The method holds the concept that when a certain GA is stuck in local minimum, then another GA might provide a feasible path point to allow it to search again. Hacioglu and Ozkol BIB002 and Hacioĝlu andÖzkol BIB001 solved the problem by introducing an idea, that is, periodically applying a vibrational mutation operator to the whole population. Therefore, it becomes possible to escape from local optimums and then to obtain a global optimal path. To avoid time consumption with a high burden, authors in BIB004 proposed a combination of GA and Voronoi method. The Voronoi diagrams are constructed by using fuzzy -means clustering method to generate the first generation, which accelerates the convergence speed. RRT obtains the merits of random exploring, which can enable escaping of local optimum. Authors in BIB003 employed RRT to generate the first generation of chromosomes to achieve global optimality.