scholarly journals Intelligent Online Path Planning for UAVs in Adversarial Environments

10.5772/45604 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 3 ◽  
Author(s):  
Xingguang Peng ◽  
Demin Xu
Keyword(s):  
Path Planning ◽  
Predictive Control ◽  
Optimization Problem ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Pareto Sets ◽  
Basic Issue ◽  
Solution Selection ◽  
Path Planner ◽  
Adversarial Environments

Online path planning (OPP) for unmanned aerial vehicles (UAVs) is a basic issue of intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, an OPP framework is proposed in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DMOP involved in the MPC we propose a dynamic multi-objective evolutionary algorithm based on linkage and prediction (LP-DMOEA). Within this algorithm, the historical Pareto sets are collected and analysed to enhance the performance. For intelligently selecting the best path from the output of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimization objective. The DMOEA is validated on three benchmark problems characterized by different changing types in decision and objective spaces. Moreover, the simulation results show that the LP-DMOEA overcomes the restart method for OPP. The decision-making method for solution selection can assess the situation in an adversarial environment and accordingly adapt the path planner.

scholarly journals Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10044-10052 ◽  
Author(s):  
Syrine Belakaria ◽  
Aryan Deshwal ◽  
Nitthilan Kannappan Jayakodi ◽  
Janardhan Rao Doppa
Keyword(s):  
Optimization Problem ◽  
State Of The Art ◽  
Selection Method ◽  
Bayesian Optimization ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Blackbox Optimization ◽  
Expensive Function ◽  
Area Overhead ◽  
Measure Of Uncertainty

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.

Effective Generation of Pareto Sets Using Genetic Programming

2001 ◽  
Author(s):  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Programming ◽  
Optimization Problem ◽  
Complete Solution ◽  
Pareto Frontier ◽  
Multi Objective ◽  
Pareto Sets ◽  
Effective Generation ◽  
Final Design ◽  
The Individual ◽  
Measure Of Performance

Abstract Many designers concede that there is typically more than one measure of performance for an artifact. Often, a large system is decomposed into smaller subsystems each having its own set of objectives, constraints, and parameters. The performance of the final design is a function of the performances of the individual subsystems. It then becomes necessary to consider the tradeoffs that occur in a multi-objective design problem. The complete solution to a multi-objective optimization problem is the entire set of non-dominated configurations commonly referred to as the Pareto set. Common methods of generating points along a Pareto frontier involve repeated conversion of multi-objective problems into single objective problems using weights. These methods have been shown to perform poorly when attempting to populate a Pareto frontier. This work presents an efficient means of generating a thorough spread of points along a Pareto frontier using genetic programming.

scholarly journals A Decision Making Approach for Multi-Objective Optimization Considering A Trade-off Method

2017 ◽  
Vol 11 (2) ◽  
pp. 178-189
Author(s):  
Tipwimol Sooktip ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Decision Maker ◽  
Optimization Problem ◽  
Benchmark Problems ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Multi Objective ◽  
Component Allocation ◽  
Objective Value ◽  
Preferred Solutions

In multi-objective optimization problem, a set of optimal solutions is obtained from an optimization algorithm. There are many trade-off optimal solutions. However, in practice, a decision maker or user only needs one or very few solutions for implementation. Moreover, these solutions are difficult to determine from a set of optimal solutions of complex system. Therefore, a trade-off method for multi-objective optimization is proposed for identifying the preferred solutions according to the decision maker’s preference. The preference is expressed by using the trade-off between any two objectives where the decision maker is willing to worsen in one objective value in order to gain improvement in the other objective value. The trade-off method is demonstrated by using well-known two-objective and three-objective benchmark problems. Furthermore, a system design problem with component allocation is also considered to illustrate the applicability of the proposed method. The results show that the trade-off method can be applied for solving practical problems to identify the final solution(s) and easy to use even when the decision maker lacks some knowledge or not an expert in the problem solving. The decision maker only gives his/her preference information.  Then, the corresponding optimal solutions will be obtained, accordingly.

A Multi-Objective PSO Algorithm Based Niche Technique for Service-Workflows Scheduling

2013 ◽  
Vol 811 ◽  
pp. 487-494 ◽  
Author(s):  
Xiao Dong Zhang ◽  
Jin Cheng Zhang ◽  
Fan Yu Zeng
Keyword(s):  
Optimization Problem ◽  
Cost Optimization ◽  
Pso Algorithm ◽  
Optimal Solutions ◽  
Time Cost ◽  
High Quality ◽  
Multi Objective ◽  
Pareto Sets ◽  
External Population ◽  
Single Objective

Time-cost optimization problem in service-workflows is a widely existing problem hard to be solved in practical systems. In this paper, an Niche Technique multi-objective PSO method is proposed. Generating initial optimal solutions with single-objective. External Population and Niche Technique and Meshing hybrid method is introduced to obtain an evenly distributed Pareto set. Experimental results prove that the proposed algorithm is efficient and effective. For various characteristic instances, more evenly distributed Pareto Sets are obtained with high quality.

scholarly journals Balancing Exploration and Exploitation With Decomposition-Based Dynamic Multi-Objective Evolutionary Algorithm

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Qing Zhang ◽  
Ruwang Jiao ◽  
Sanyou Zeng ◽  
Zhigao Zeng
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Multi Objective ◽  
Count Function

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.

Closed-loop control of bevel-tip needles based on path planning

Robotica ◽  
2018 ◽  
Vol 36 (12) ◽  
pp. 1857-1873
Author(s):  
Benyan Huo ◽  
Xingang Zhao ◽  
Jianda Han ◽  
Weiliang Xu
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimization Problem ◽  
Closed Loop ◽  
Control Method ◽  
Kinematic Model ◽  
Planning Method ◽  
Closed Loop Control ◽  
Loop Control ◽  
Multi Objective

SUMMARYBevel-tip needles have the potential to improve paracentetic precision and decrease paracentetic traumas. In order to drive bevel-tip needles precisely with the constrains of path length and path dangerousness, we propose a closed-loop control method that only requires the position of the needle tip and can be easily applied in a clinical setting. The control method is based on the path planning method proposed in this paper. To establish the closed-loop control method, a kinematic model of bevel-tip needles is first presented, and the relationship between the puncture path and controlled variables is established. Second, we transform the path planning method into a multi-objective optimization problem, which takes the path error, path length and path dangerousness into account. Multi-objective particle swarm optimization is employed to solve the optimization problem. Then, a control method based on path planning is presented. The current needle tip attitude is essential to plan an insertion path. We analyze two methods to obtain the tip attitude and compare their effects using both simulations and experiments. In the end, simulations and experiments in phantom tissue are executed and analyzed, the results show that our methods have high accuracy and have the ability to deal with the model parameter uncertainty.

The Research on Path Planning Algorithm of RoboCup Based on PFNPGA

2010 ◽  
Vol 20-23 ◽  
pp. 1192-1198
Author(s):  
Xian Yi Cheng ◽  
Qian Zhu ◽  
Zhen Wen Zhang
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Planning Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Poor ◽  
Starting Point ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Path Planning Algorithm

To improve the poor efficiency in path planning that caused by not taking RoboCup’s stamina, character, dynamic starting point, dynamic endpoint and other factors into consideration in the path planning process, the RoboCup path planning is generalized as a multi-objective optimization problem in the paper, and proposes RoboCup’s sport model with dynamic multi-objective path planning which is based on RoboCup’s stamina triple model, and a path planning algorithm that is suited for RoboCup is advanced based on PFNPGA ( Penalty Function Niche Pareto Genetic Algorithm）. The experiment in a real environment shows that, by comparing with traditional path planning methods, the algorithm in the paper can get more reasonable path at the premise of guarantee RoboCup have relative high stamina values.

scholarly journals Balancing Exploration and Exploitation with Decomposition-Based Dynamic Multi-objective Evolutionary Algorithm

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Multi Objective ◽  
Count Function

Balancing exploration and exploitation is a crucial issue in evolutionary global optimization. This paper proposes a decomposition-based dynamic multi-objective evolutionary algorithm for addressing global optimization problems. In the proposed method, the niche count function is regarded as a helper objective to maintain the population diversity, which converts a global optimization problem to a multi-objective optimization problem (MOP). The niche-count value is controlled by the niche radius. In the early stage of evolution, a large niche radius promotes the population diversity for better exploration; in the later stage of evolution, a niche radius close to 0 focuses on convergence for better exploitation. Through the whole evolution process, the niche radius is dynamically decreased from a large value to zero, which can provide a sound balance between the exploration and exploitation. Experimental results on CEC 2014 benchmark problems reveal that the proposed algorithm is capable of offering high-quality solutions, in comparison with four state-of-the-art algorithms.

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