31 research outputs found
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
Modified predator-prey (MPP) algorithm for single-and multi-objective optimization problems
The aim of this work is to develop an algorithm that can solve multidisciplinary design optimization problems. In predator-prey algorithm, a relatively small number of predators and a much larger number of prey are randomly placed on a two dimensional lattice with connected ends. The predators are partially or completely biased towards one or more objectives, based on which each predator kills the weakest prey in its neighborhood. A stronger prey created through evolution replaces this prey. In case of constrained problems, the sum o f constraint violations serves as an additional objective.
Modifications of the basic predator-prey algorithm have been implemented in this study regarding the selection procedure, apparent movement of the predators, mutation strategy, dynamics of the Pareto convergence, etc. Further modifications have been made making the algorithm capable of handling equality and inequality constraints. The final modified algorithm is tested on standard constrained/unconstrained, single and multi-objective optimization problems
Conceptual Design of Cellular Auxetic Systems with Passive Adaptation to Loading
Auxetics refer to a class of engineered structures which exhibit an overall
negative Poisson's ratio. These structures open up various potential
opportunities in impact resistance, high energy absorption, and flexible
robotics, among others. Interestingly, auxetic structures could also be
tailored to provide passive adaptation to changes in environmental stimuli --
an adaptation of this concept is explored in this paper in the context of
designing a novel load-adaptive gripper system.
Defining the design in terms of repeating parametric unit cells from which
the finite structure can be synthesized presents an attractive
computationally-efficient approach to designing auxetic structures. This
approach also decouples the optimization cost and the size of the overall
structure, and avoids the pitfalls of system-scale design e.g., via topology
optimization. In this paper, a surrogate-based design optimization framework is
presented to implement the concept of passively load-adaptive structures (of
given outer shape) synthesized from auxetic unit cells. Open-source meshing,
FEA and Bayesian Optimization tools are integrated to develop this
computational framework, enhancing it adopt-ability and extensibility.
Demonstration of the concept and the underlying framework is performed by
designing a simplified robotic gripper, with the objective to maximize the
ratio of towards-load (gripping) horizontal displacement to the load-affected
vertical displacement. Optimal auxetic cell-based design generated thereof is
found to be four times better in terms of exhibited contact reaction force when
compared to a design obtained with topology optimization that is subjected to
the same specified maximum loading.Comment: Presented at (and accepted for publication in the proceedings of)
International Design Engineering Technical Conferences & Computers and
Information in Engineering Conference (IDETC/CIE) 202