Survey of Robot 3D Path Planning Algorithms <s> Bioinspired Algorithms: Elements and Analysis. <s> An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS. <s> BIB001 </s> Survey of Robot 3D Path Planning Algorithms <s> Bioinspired Algorithms: Elements and Analysis. <s> Shuffled Frog Leaping Algorithm (SFLA) is a meta-heuristic for solving discrete optimization problems. Here it is applied to determine optimal discrete pipe sizes for new pipe networks and for network expansions. SFLA is a population based, cooperative search metaphor inspired by natural memetics. The algorithm uses memetic evolution in the form of infection of ideas from one individual to another in a local search. The local search is similar in concept to particle swarm optimization. A shuffling strategy allows for the exchange of information between local searches to move toward a global optimum. This paper summarizes the development of SFLANET, a computer model that links SFLA and the hydraulic simulation software EPANET and its library functions. Application of SFLANET to literature network design problems is then described. Although the algorithm is in its initial stages of development, promising results were obtained. <s> BIB002 </s> Survey of Robot 3D Path Planning Algorithms <s> Bioinspired Algorithms: Elements and Analysis. <s> Abstract In this paper, optimal paths in environments with obstacles for underwater vehicles are computed using a numerical solution of the nonlinear optimal control problem (NOCP). The underwater vehicle is modeled with six-dimensional nonlinear and coupled equations of motion, controlled by DC motors in all degrees of freedom. An energy performance index combined with a time consumption index is used. Both fixed and free final times are considered. Solving NOCP leads to a two point boundary value problem (TPBVP). Five intelligent evolutionary algorithms (EAs), which include genetic algorithm, memetic algorithm, particle swarm optimization, ant colony optimization and shuffled frog leaping algorithm, are applied to solve the NOCP. For comparison, a conjugate gradient penalty method is also used to solve the TPBVP. The simulation results show that the trajectories obtained by the intelligent methods are better than those of conjugate gradient method. After analyzing a simple path planning problem, the time-energy-optimal path planning problem in energetic environments is propounded and solved by EAs. The problem of static obstacle collision avoidance in an energetic environment is also studied. <s> BIB003
Bioinspired algorithms originate from mimicking biological behavior to solve problems. This kind of planning methods leaves out the process of constructing complex environment models to search a near optimal path based on stochastic approaches; it overcomes the weakness where general mathematic model based algorithms often fail (or drop into local minimum) in solving NP-hard problem with large number of variables and nonlinear objective functions BIB003 . Figure 6 presents a set of typical current methods belonging to this category. Where GA is Genetic Algorithm  , it is the most famous population-based numerical optimization method; MA represents Memetic Algorithm  ; it is a populationbased heuristic exploring approach for combinatorial optimization problems; PSO is particle swarm optimization  ; it is a population-based stochastic optimization algorithm; ACO is Ant Colony Optimization BIB001 that imitates the behavior of ant in finding the shortest path by using pheromone information; and SFLA is Shuffled Frog Leaping Algorithm BIB002 which is the combination of MA and PSO. For bioinspired algorithms, they are divided into Evolutionary Algorithm (EA) and Neural Network (NN) due to the fact that they are analyzed at different level, which will be discussed in detail in the following sections.