Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and use of a performance-based approach in transportation planning and programming. This research introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to (1) improve bridge management, (2) support state departments of transportation with their transition efforts to comply with the MAP-21 requirements, (3) determine short- and long-term intervention strategies and funding requirements, and (4) facilitate trade-offs between funding levels and performance. The proposed methodology focuses on one transportation asset class (i.e., bridge) and is structured around the following five modules: 1. Data Processing Module, 2. Improvement Module, 3. Element-level Optimization Module, 4. Bridge-level Optimization Module, and 5. Network-level Optimization Module. To overcome computer memory and processing time limitations, the methodology relies on the following three distinct screening processes: 1. Element Deficiency Process, 2. Alternative Feasibility Process, and 3. Solution Superiority Screening Process. The methodology deploys an independent deterioration model (i.e., Weibull/Markov model), to predict performance, and a life-cycle cost model, to estimate life-cycle costs and benefits. Life-cycle (LC) alternatives (series of element improvement actions) are generated based on a new simulation arrangement for three distinct improvement types: 1. maintenance, repair and rehabilitation (preservation); 2. functional improvement; and 3. replacement. A LC activity profile is constructed separately for each LC alternative action path. The methodology consists of three levels of optimization assessment based on the Pareto optimality concept: (1) an element-level optimization, to identify optimal or near-optimal element intervention actions for each deficient element (poor condition state) of a candidate bridge; (2) a bridge-level optimization, to identify combinations of optimal or near-optimal element intervention actions for a candidate bridge; and (3) a network-level optimization, following either a top-down or bottom-up approach, to identify sets of optimal or near-optimal element intervention actions for a network of bridges. A robust metaheuristic genetic algorithm (i.e., Non-dominated Sorting Genetic Algorithm II, [NSGA-II]) is deployed to handle the large size of multi-objective optimization problems. A MATLAB-based tool prototype was developed to test concepts, demonstrate effectiveness, and communicate benefits. Several examples of unconstrained and constrained scenarios were established for implementing the methodology using the tool prototype. Results reveal the capability of the proposed EB-MOO methodology to generate a high quality of Pareto optimal or near-optimal solutions, predict performance, and determine appropriate intervention actions and funding requirements. The five modules collectively provide a systematic process for the development and evaluation of improvement programs and transportation plans. Trade-offs between Pareto optimal or near-optimal solutions facilitate identifying best investment strategies that address short- and long-term goals and objective priorities

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Supply Chain Planning with Incremental Development, Modular Design, and Evolutionary Updates

Proceedings Paper (for Acquisition Research Program)The policy specified by DoDI 5000.02 (DoD, 2008, December 8) prescribes an evolutionary acquisition strategy. Products with long lifecycles such as torpedoes, evolutionary updates via incremental development, modular design updates, technology refreshes, technology insertions, and Advanced Processor Builds are all in play at the same time. Various functional elements of the weapon system are often redesigned during the lifecycle to meet evolving requirements. Component obsolescence and failures must also be anticipated and addressed in upgrade planning. Within each weapon system''s evolutionary acquisition, cycle-changing requirements may expose weaknesses that have to be rectified across the inventory. New acquisition paradigms such as modular design have to be introduced into the supply chain while maintaining inventory levels of previously designed weapons at a high level of readiness. Thus, a diverse set of requirements must be satisfied with a finite set of resources. The acquisition policy does not provide guidance on how to address cross-coordination and optimization of project resources. This paper explores decision models for balancing conflicting demands and discusses the application of how these models address cross-coordination and optimization of project resources in the torpedo acquisition process while keeping the weapon''s efficiency and inventory effectiveness at or above minimum specified levels.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited