A fuzzy adaptive differential evolution for multi-objective 3D UAV path optimization

Adaptive optimization of virtual synchronous generator based on fuzzy logic control and differential evolution

The research presented in this article focuses on the development of a multi-objective optimization algorithm based on the differential evolution (DE) concept combined with Mamdani-type fuzzy logic controllers (FLCs) and $$K$$ K -medoids clustering. The FLCs are used for adaptive control of the DE parameters; $$K$$ K -medoids clustering enables the algorithm to perform a more guided search by evolving neighboring vectors, i.e., vectors that belong to the same cluster. A modified version of the $$DE/best/1/bin$$ D E / b e s t / 1 / b i n algorithm is adopted as the core search component of the multi-objective optimizer. The FLCs utilize Pareto dominance and cluster-related information as input in order to adapt the algorithmic parameters dynamically. The proposed optimization algorithm is tested using a number of problems from the multi-objective optimization literature in order to investigate the effect of clustering and parameter adaptation on the algorithmic performance under various conditions, e.g., problems of high dimensionality, problems with non-convex Pareto fronts, and problems with discontinuous Pareto fronts. A detailed performance comparison between the proposed algorithm with state-of-the-art multi-objective optimizers is also presented.

Differential evolution (DE) is an efficient and robust evolutionary algorithm, which has been widely and successfully applied to solve global optimization problems. Although many methods have been developed based on the population topology to improve the performance of DE, the effects of population topology interacted with the functions being optimized are not considered in most of the algorithm designs. Moreover, the synergy among multiple population topologies has not been systematically exploited in DE. In this paper, a novel DE algorithm with neighborhood-dependent mutation operator, named neighborhood-dependent DE (NDE), is proposed. In NDE, a pool of population topologies is used to define multiple neighborhood relationships for each individual and then the neighborhood relationships are adaptively selected for the functions being solved during the evolutionary process. The experimental results on the benchmark functions from CEC2013 demonstrate that NDE is able to enhance the performance of the studied DE algorithms. Additionally, the synergy among different neighborhood topologies in NDE is also revealed.

Robots are widely used in industry due to their efficiency and high accuracy in performance. One of the most intriguing issues in manufacturing stage of production line is to minimize significantly high percentage of energy consumed by these robot manipulators. The energy optimal control of robotic manipulators is a complex problem, as it requires a deep understanding of the robot's kinematics and dynamic behaviors. This paper propose a novel method of energy efficient path planning of an industrial robot arm in a workspace with multiple obstacles using differential evolution (DE) algorithm. The path-planning problem is formulated as an optimization problem with an aim to determine the shortest and energy optimal path of the robot arm from its given initial position to the predefined goal location, without hitting obstacles. Application of such evolutionary algorithms in trajectory planning is advantageous because the exact solution to the path-planning problem is not always available beforehand and must be determined dynamically. Experiments undertaken reveal that the DE-based path-planning algorithm outperforms its contenders in a significant manner.

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

With complex dynamic characteristics in many variables changing during actual operation, a Mecanum-wheeled mobile robot (MWMR) causes many difficulties for path planning and control. In addition, in many applications, robots need to operate automatically and find the optimal path. This article proposes intelligent MWMR path planning based on the real-time rapidly exploring random tree* (RT-RRT*) and optimal interval type 2 fuzzy logic controller (IT2FLC). First, the path planning of the MWMRs in dynamic environments is developed by RT-RRT*. Second, the optimal IT2FLC is designed for MWMRs based on the genetic algorithm. The pre-treatment and post-treatment coefficients of IT2FLC are optimized with the goal of achieving the best trajectory quality of the robot. This helps the robot to operate independently and accurately, and complete tasks in environments with many complex disturbances. Various trajectories are given to test the performance of the proposed approach. The results show that the efficiency of the designed method is better than that of a type 1 fuzzy logic controller (traditional fuzzy logic controller) and proportional–integral–derivative under the same conditions.

Until now, numerous mutation strategies have been introduced as search operators within the differential evolution (DE) algorithm. These operators are designed mainly to improve fitness value while also maintaining diversity in the population, but they do not directly act to reduce constraint violations of constrained problems. Interestingly, the so-called constraint handling techniques, used with most evolutionary algorithms, are not a part of the actual search process. Instead, the constraint violations are only considered in the ranking and selection of individuals for participation in the search process. This paper introduces a new DE mutation operator that incorporates a mechanism, based on constraint consensus, that can directly help to reduce the constraint violations during the evolutionary search process. The proposed DE algorithm has been tested on a set of well-known constrained benchmark problems. The experimental results show that the proposed algorithm is able to obtain better solutions, compared to the standard DE algorithm, with significantly reduced computational effort. The algorithm also outperforms state-of-the-art algorithms.

A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem

Evolving ant direction differential evolution for OPF with non-smooth cost functions

Optimal design of linear phase multi-band stop filters using improved cuckoo search particle swarm optimization

Unmanned Aerial Vehicles (UAVs) are used in numerous military and civil application areas, and they have gained prominence in the research community. A UAV has to operate in a complex environment and checks the control points in the mission area by satisfying different constraints of the assigned task. Therefore, the path planning problem is one of the important areas in UAV researches. If the number of control points increases in the Unmanned Aerial System (UAS), finding a feasible solution in this large search space takes up a great deal of time. Nowadays low-cost UAVs are available, and this enables the use of multi-UAV systems to perform different tasks more efficiently and quickly. This usage increases the complexity of effective path planning and task allocation problem. This paper presents a flyable path planning for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment at a constant altitude. A feasible path is firstly calculated by GAs, and then this path is smoothed by using Bezier curves. Experimental results indicate that the proposed approach produces effective and feasible paths for each UAV in a multi-UAV system. System is implemented in Java with a GUI for presenting results. The paper also draws future works that can be done on this topic.

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners—In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms.

A Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblems and solves them using information from neighboring subproblems. MOEA/D variants with other evolutionary operators have emerged over the years, improving their efficiency in various MOPs. Recently, the IWOA (Improved Whale Optimization Algorithm) was proposed, an optimization algorithm bioinspired by the whale hunting method hybridized with Differential Evolution, which presented excellent results in single-objective optimization problems. This work proposes the MOEA/D-IWOA algorithm, which associates characteristics of the evolutionary operators of the IWOA to MOEA/D. Computational experiments were accomplished to analyze the performance of the MOEA/D-IWOA in benchmark MOPs suites. The results were compared with those obtained by the MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2), and Indicator-Based Evolutionary Algorithm (IBEA) algorithms in the Hypervolume and Inverted Generational Distance Plus (IGD+) indicators. The MOEA/D-IWOA proved to be competitive, with a good performance profile, in addition to presenting the best results in some POMs.

Cable-driven robots are characterized by a large workspace, high speed, and load-to-weight ratio, providing a new technology approach for large-scale autonomous 3D construction of lunar surface shelters. A novel deployable cable-driven construction 3D printer (CDCP) is developed in this study. Guiding pulleys are considered and modeled to improve the accuracy of the system. A fuzzy adaptive differential evolution PID (FDEPID) control is proposed to reduce end-effector motion errors in Cartesian space and thus improve printing quality and stability. Concentric and zigzag infill strategies are compared to optimize printing efficiency and infill effectiveness in path planning. The simulations focus on evaluating the effects of pulley kinematics on trajectory tracking, evaluating system dynamics under low-gravity conditions, and examining the effectiveness of motion control and path planning methods. Finally, experiments are carried out to validate the proposed FDEPID control and path planning strategies which demonstrate the great potential of the developed cable-driven printer in the construction of lunar architecture.

The studies of Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems have received much attention due to their wide applications in the disaster rescue, target tracking, intelligent surveillance, and automatical package delivery missions. UAVs provide a broad view and have a fast speed in the air, while UGVs have sufficient load capacity and can serve as repeater stations on the ground. The path planning of a UAV/UGV cooperative system is an important but difficult issue, which aims to plan paths for both the UAVs and the UGVs in the system to cooperatively complete a mission. In this article, we consider the path planning problem of the UAV/UGV cooperative system for illegal urban building detection, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. To solve this problem, we first model the path planning problem as a constraint optimization problem which tries to minimize an overall execution time for completing the illegal urban building detection tasks, and then propose a two-level memetic algorithm (called Two-MA) to solve the path planning problems of both the UAV and the UGV. Experiments on both synthetic and real-world data sets show the superiority of the proposed Two-MA over several states-of-the-art algorithms in solving the path planning problems of the UAV and UGV for illegal urban building detection tasks. Note to Practitioners—This article was motivated by the task of detecting illegal buildings in cities by unmanned vehicles. Previous works mainly focus on path planning of either UAVs or UGVs in this task. This article proposes a new approach using an Unmanned Air/Ground Vehicle (UAV/UGV) cooperative system for detecting illegal buildings in parks, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. This cooperative system consists of a UAV, UGV, and control center. The UAV equipped with cameras takes aerial photography in the air, and can transmit collected photos to the control center. The UGV executes loading and transportation on the ground, and can serve as takeoff and landing platforms for the UAV. The control center executes computationally intensive tasks such as data transmission and processing, task scheduling, and vehicle coordination. To quickly complete all detection tasks, a memetic algorithm is proposed for path planning of both the UAV and the UGV. The simulated results show that the proposed algorithm enables the UAV/UGV cooperative system to visit all buildings in cities with a minimum task execution time.