In this paper, we want to solve multi-objective robot path planning problem. A new elitist multi-objective approach is proposed to determine Pareto front based on coefficient of variation. Intelligent water drops (IWD) algorithm is generalized by this approach and as a new multi-objective IWD algorithm. We call our new algorithm CV-based MO-IWD. It tried to optimize two objectives: length and safety of the path. In the CV-based MO-IWD, we want to discover solutions as close to optimal Pareto solutions as possible and find solutions as diverse as possible in the obtained Pareto front. In this way, coefficient of variation of Pareto front is determined in each objective. Then, appropriate number of heuristic operations (local search in this paper) is calculated and applied for each solution. Implementation results and comparisons with NSGA_II algorithm show the ability of the proposed approach to achieve a near optimal Pareto front with a good diversity, while the number of fitness function calls does not increase. This method is superior because of suitable distribution of heuristic operations.

In this paper, a generalized intelligent water drops algorithm (IWD) for solving robot path planning problem is proposed. The authors want to reduce the time of reaching the optimal solution as much as possible. To do this, some new heuristic operators and a multi section graph model of environment is introduced. The authors divide graph to equal sections and compare behaviour of the solutions (paths) in each section with behaviour of them in other sections. This comparison uses a fuzzy inference system. Base on this comparison, a fuzzy number is assigned to each part of solutions. This fuzzy number determines the worth of a solution in a section. Less worth solutions need more improvement. New heuristic operators are used in the sections that need more improvement. The runtime of algorithms are increased by using a memory for keep proper solutions and a global smooth operator that smooth the solutions. The authors used proposed memory to apply heuristic operations on proper solutions more than other one. This method helps to obtain more improvement in lower runtime. The authors introduce two intersection operators for robot path planning problem that apply to solutions in memory. Experimental results show that the proposed algorithms find the optimal solutions in fewer runtime rather than other works.

In this paper, the generalized intelligent water drops (IWD) algorithm is proposed to solve robot path planning problem. Path planning is modeled by IWD algorithm, because there is inherent similarity between this and finding most appropriate route in natural rivers, that occurs by interaction between water drops and the river bed. The proposed algorithm has two levels; first level, finds the best global feasible path. Second level, performs local search at relatively near distances of global path and reduces its length and time to reach optimal solution. The IWD algorithm, like other inspired nature algorithms, has not a mechanism to deal with constrained optimization problem in its original version. So, a mechanism has been proposed based on repair of infeasible solutions. In this mechanism, a fuzzy inference system is proposed for selecting the best possible infeasible solution and then the local search operator repairs any of them in violation locations. Another innovation of this paper provides a mechanism to local soil update, which is based on fuzzy systems and is independent of size and complexity of environment and its soil altering will be defined in fixed range. Simulation result shows ability of IWD algorithm in finding of the optimal path.

In this paper, generalized intelligent water drops (IWD) algorithm is proposed to solve robot path planning problem. The proposed algorithm has two levels; first level, finds best global path. Second level, performs local search at relatively near distances of global path and reduces its length and response time. IWD algorithm, like other nature-inspired algorithms, has not a mechanism to deal with constrained optimization problem. So, a mechanism has been proposed based on repair of infeasible solutions. In this mechanism, proposed local search operator repairs some of infeasible solutions. Simulation result shows ability of IWD algorithm in finding the optimal path.

Nowadays due to increased traffic congestion, travel time problem in the urban road network is one of the most important issues in big cities. A significant part of travel time is related to time that delayed in intersections. Hence, the issue of intersections control is important to reduce delays. Receiving traffic information is one of usual method by using cameras, sensors and image processors and applying fuzzy logic to obtain the optimal schedule. The purpose of this paper is to achieve the optimum timing for the intersection, by using available data from Vehicular Ad hoc Network and to extent the fuzzy rules for intersection control mechanism. So that high priority is given to routes with higher density and thus urban traffic congestion is reduced. In addition to traffic information that is accessible through image processors, other information such as waiting time and stop any of the vehicles is also available on VANET. The proposed control mechanism can be modeled using Petri Net and evaluate of suggestions will be possible with the help of this method.