Heuristic search is a powerful approach that has successfully been applied to broad class of planning problems, including classical planning, multi-objective and probabilistic modelled as stochastic shortest path (SSP) problem. Here, we extend the reach heuristic more expressive namely paths (MOSSPs), which require computing coverage set non-dominated policies. We design new algorithms MOLAO* M...

introduction: one of the most important parameters in x-ray ct imaging is the noise induced by detected scattered radiation. the detected scattered radiation is completely dependent on the scanner geometry as well as size, shape and material of the scanned object. the magnitude and spatial distribution of the scattered radiation in x-ray ct should be quantified for development of robust scatter...

Objectives . A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other indices, whose values are thus preset. For scalar objective function, global minimum sought that contains restricted indices as penalty terms. However, landscape such function has steep-ascent areas, which significantly complicate search minimum. T...

Abstract Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective may have in single-objective space. For this purpose, we examine inner mechanisms of recently developed sophisticated local search procedure SOMOGSA. method solves problems based first expanding problem an additional ob...

This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a ...

There is renewed interest in the development of effective and efficient methods for optimizing models of which the optimizer has no structural knowledge. This is what in the literature is referred to as optimization of black boxes. In particular, we address the challenge of optimizing expensive black boxes, that is, those that require a significant computational effort to be evaluated. We descr...