We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using ...

In this paper we present a family of multi-objective hypergraph partitioning algorithms based on the multilevel paradigm, which are capable of producing solutions in which both the cut and the maximum subdomain degree are simultaneously minimized. This type of partitionings are critical for existing and emerging applications in VLSI CAD as they allow to both minimize and evenly distribute the i...

Good algorithms exist for solving the 2D rectangular strip packing problem when the objective is to minimize the amount of wasted material. However, in some applications other criteria are also important. We describe new heuristics for strip packing that optimize not only for wastage, but also for the efficient use of the cutting equipment, by minimizing the number of independent cuts required ...

Many real-world tasks require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent systems need planning or learning algorithms that can e ciently find di↵erent ways of balancing the trade-o↵s that such objectives present. In this tutorial, we provide an introduction to decision-theoretic approaches to coping with multiple objectives. We ...

In multi-objective reinforcement learning (MORL) the agent is provided with multiple feedback signals when performing an action. These signals can be independent, complementary or conflicting. Hence, MORL is the process of learning policies that optimize multiple criteria simultaneously. In this abstract, we briefly describe our extensions to single-objective multi-armed bandits and reinforceme...

The users of shape segmentation algorithms possess a wealth of knowledge about the objects they wish to segment. Current automatic segmentation approaches, however, apply a fixed objective uniformly to all parts and limit their input to the number of segments desired and a small set of parameter values. In this paper, we propose the concept of multi-objective shape segmentation. This model allo...

Conventional multi-agent path planners typically determine a that optimizes single objective, such as length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria not readily compared sometimes lie competition with each other. Simply applying standard multi-objective search...

In this paper, we focus on the symmetrical relationship between multi-properties objective reachability problems and multi-objective sets over FKS based Zadeh logic. First, give formal definitions of those two problems. Then, study their relationships find that a problem special case have symmetry. Finally, polynomial time algorithm symmetry to convert problem. addition, an illustrative example...