In real-time computer vision, there is a need for classifiers that detect patterns fast and reliably. We apply multi-objective optimization (MOO) to the design of feed-forward neural networks for real-world object recognition tasks, where computational complexity and accuracy define partially conflicting objectives. Evolutionary structure optimization and pruning are compared for the adaptation...

In many real world optimization problems, several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in multi-objective optimization (MOO) in the past many years. Several new approaches have recently been proposed, which produced very good results. However, existing techniques have solved mainly problems of “low dimension”, i.e., with less t...

Traditionally, single-objective and multi-objective optimization have only considered a single problem in one run. However, the notion of evolutionary multitasking, which aims at solving multiple optimization problems simultaneously, has recently emerged in Evolutionary Computation (EC). It is inspired by the implicit parallelism of population-based search, which attempts to take advantage of i...

Design by optimization of curves and surfaces is a powerful design technique. The mathematical modeling of the design problem often results in a multi-objective optimization (MOO) problem which cannot be handled as such by traditional single objective optimization algorithms. Hence an evolutionary MOO algorithm is used to handle this case. The use of MOO algorithm enhances the design process by...

Title: Analysis of manufacturing supply chains using system dynamics and multi-objective optimization I ABSTRACT Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. Hence, due to these multiple performance measures, supply chain...

As predictive capabilities advance and human-model fidelity increases, so must validation of such predictions and models. However, subjective validation is sufficient only as an initial indicator; thorough, systematic studies must be conducted as well. Thus, the purpose of this paper is to validate postures that are determined using single-objective optimization (SOO) and multi-objective optimi...

Many practical optimization problems, especially in robotics, involve multiple competing objectives, e.g. performance metrics such as speed and energy efficiency. Proper treatment of these objective functions is often overlooked. Additionally, optimization of the performance of robotic systems can be restricted due to the expensive nature of testing control parameters on a physical system. This...

Machine learning often requires the optimization of multiple, partially conflicting objectives. True multi-objective optimization (MOO) methods avoid the need to choose a weighting of the objectives a priori and provide insights about the tradeoffs between the objectives. We extend a state-of-the-art derivative-free Monte Carlo method for MOO, the MO-CMA-ES, to operate on an unbounded set of (n...

Multi-objective optimization (MOO) is the process of optimizing multiple objective functions concurrently and systematically. Modern Army missions require a tactical network to achieve multiple objectives as multiple parties with different objectives are involved in collaborative mission execution. MOO problems have been studied extensively in the field of coalition formation based on evolution...

in this paper, experimentally derived correlations of heat transfer and pressure drop are used in a pareto based multi-objective optimization (moo) approach to find the best possible combinations of heat transfer and pressure drop of tio2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. in this study there are four independent design variables: t...