Xingguang Peng

Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments

In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.

Intelligent Online Path Planning for UAVs in 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.

The routing problem of autonomous underwater vehicles in ocean currents

This paper considers a novel algorithm for the routing problem of autonomous underwater vehicles (AUVs) in order to deliver customized sensor packages to mission targets at scattered positions. We aim to utilize a set of AUVs to serve all the targets for exactly once on the premise of individual limited sensor packages loading ability while guaranteeing the least total energy cost in the presence of ocean currents. The main idea of the algorithm is based on the decomposition of the initial routing problem into two subproblems: the assignment of targets and the generation of sub-path between the targets. We present an integrated mission assignment and path planning algorithm which is proposed by combing the branch and bound method and a velocity synthesis approach. The effectiveness and efficiency of the proposed algorithm are verified by simulation results.

Intelligent flight for UAV via integration of dynamic MOEA, Bayesian network and fuzzy logic

Intelligent flight is a key technology for an unmanned aerial vehicle (UAV) to react to the changing environment. Online path planning (OPP) is a basic issue for intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, we use an OPP scheme in the sense of model predictive control to continuously update the environmental information for the planner. This method is in fact a DMOP. For solving the problem at hand 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 analyzed to enhance the performance. For intelligently selecting the best path from the output (a set of Pareto solutions obtained by the LP-DMOEA) of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimal objective. The experimental results show the LP-DMOEA works more effectively for OPP in contrast to the restart method and the intelligent methods for solution selection can automatically assess the changing environment and adapt the path planner.

Bio-inspired navigation based on geomagnetic for the autonomous underwater vehicle

Dependence on the prior magnetic map become one of the key problems which restrict the development of the geomagnetic navigation. This paper inspired from the animal navigation behavior which dispense with the priori geomagnetic map. First, we generalize the bio-inspired navigation process as a multi-objective problems. Then, present a stress evolution search AUV navigation model for the particularity of geomagnetic navigation, which make multi-objective problem solving search combine with navigation movement to achieve the purpose of navigation. Finally, compared the present model with a gradient descent model with part priori geomagnetic map. The simulation results show that the proposed algorithm allows the AUV to navigate efficiently using geomagnetic information without prior map. The proposed algorithm offers insights into the research and application of the biologically inspired geomagnetic navigation.

Compensate information from multimodal dynamic landscapes: An anti-pathology cooperative coevolutionary algorithm

Cooperative coevolutionary algorithms (CCEAs) divides a problem into several components and optimizes them independently. Some coevolutionary information will be lost due to the search space separation. This may lead some algorithmic pathologies, such as relative overgeneralization. In addition, according to the interactive nature of the CCEA, the coevolutionary landscapes are dynamic. In this paper, a multipopulation strategy is proposed to simultaneously search local or global optima in each dynamic landscape and provide them to the other components. Besides, a grid-based archive scheme is proposed to archive these historic collaborators for reasonable fitness evaluation. Two benchmark problems were used to test and compare the proposed algorithm to three classical CCEAs. Experimental results show that the proposed algorithm effectively counteract relative overgeneralization pathology and significantly improve the rate of converging to global optimum.

Large-scale cooperative co-evolution with bi-objective selection based imbalanced multi-modal optimization

Cooperative co-evolutionary algorithm (CC) which runs in a divide-and-conquer manner is effective to solve large-scale global optimization (LSGO) problems. Multi-modal optimization (MMO) intends to locate multiple optimal solutions. Using MMO methods in CC algorithm would be beneficial, because MMO optimizer can provide more information about the landscapes. In this paper, a bi-objective selection is proposed to introduce imbalance among the subpopulations of a MMO optimizer. Only the highly representative subpopulations will be active and evolved in the MMO procedure. With this imbalanced MMO technique, the CCs subcomponents could obtain sufficient coevolutionary information (multiple optima) from each other. In addition, more computational resources could be saved and used in CC procedure. Experiments and statistical comparisons are conducted on LSGO benchmark functions to verify the effectiveness of the proposed method. The results indicate that the proposed algorithm significantly outperforms seven state-of-the-art large-scale CC algorithms.

Multi-target trapping in constrained environments using gene regulatory network-based pattern formation

Inspired by the morphogenesis of biological organisms, gene regulatory network-based methods have been used in complex pattern formation of swarm robotic systems. In this article, obstacle information was embedded into the gene regulatory network model to make the robots trap targets with an expected pattern while avoiding obstacles in a distributed manner. Based on the modified gene regulatory network model, an implicit function method was adopted to represent the expected pattern which is easily adjusted by adding extra feature points. Considering environmental constraints (e.g. tunnels or gaps in which robots must adjust their pattern to conduct trapping task), a pattern adaptation strategy was proposed for the pattern modeler to adaptively adjust the expected pattern. Also to trap multiple targets, a splitting pattern adaptation strategy was proposed for diffusively moving targets so that the robots can trap each target separately with split sub-patterns. The proposed model and strategies were verified through a set of simulation with complex environmental constraints and non-consensus movements of targets.

Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments

This chapter aims to solve the online path planning (OPP) and dynamic target assignment problems for the multiple unmanned aerial combat vehicles (UCAVs) anti-ground attack task using evolutionary algorithms (EAs). For the OPP problem, a model predictive control framework is adopted to continuously update the environmental information for the planner. A dynamic multi-objective EA with historical Pareto set linkage and prediction is proposed to optimize in the planning horizon. In addition, Bayesian network and fuzzy logic are used to quantify the bias value to each optimization objective so as to intelligently select an executive solution from the Pareto set. For dynamic target assignment, a weapon target assignment model that considers the inner dependence among the targets and the expected damage type is built up. For solving the involved dynamic optimization problems, an environment identification based memory scheme is proposed to enhance the performance of estimation of distribution algorithms. The proposed approaches are validated via simulation with a scenario of suppression of enemy air defense mission.