A New Approach on Many Objective Diversity Measurement

In multi-objective particle swarm optimization (MOPSO) methods, selecting the best {it local guide} (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. here, we introduce the Sigma method as a new method for finding best local guides for each particle of the population. The Sigma method is implemented and is compared with another method, which uses the strategy of an existing MOPSO method for finding the local guides. These methods are examined for different test functions and the results are compared with the results of a multi-objective evolutionary algorithm (MOEA)

Evolutionary population dynamics and multi-objective optimisation problems

Griffith Sciences, School of Information and Communication TechnologyFull Tex

Multi-objective Multiplexer Decision Making Benchmark Problem

This paper proposes a novel multi-objective decision making benchmark problem. The problem addresses the need in the multi-objective decision making realm for an easy to construct, scalable benchmark problem in the vain of the DTLZ, ZTD, and WFG problems. The problem is inspired by a real-world decision making problem that pilots face in the cockpit. The new problem is an amalgamation of two well-established problems within the literature, the DTLZ and multiplexer problems. The problem additionally makes use of the main concepts and ideas from Robust Decision Making and Multi-scenario Multi-objective Robust Decision Making, especially as these problems enable decision making problems to be somewhat converted into an optimization task. The problem is showcased here and is solved initially using a modified multi-objective optimization variant of a Learning Classifier System, which shows superior results when compared to a random agent

Multi-objective tree search approaches for general video game playing

The design of algorithms for Game AI agents usually focuses on the single objective of winning, or maximizing a given score. Even if the heuristic that guides the search (for reinforcement learning or evolutionary approaches) is composed of several factors, these typically provide a single numeric value (reward or fitness, respectively) to be optimized. Multi-Objective approaches are an alternative concept to face these problems, as they try to optimize several objectives, often contradictory, at the same time. This paper proposes for the first time a study of Multi-Objective approaches for General Video Game playing, where the game to be played is not known a priori by the agent. The experimental study described here compares several algorithms in this setting, and the results suggest that Multi-Objective approaches can perform even better than their single-objective counterparts

Multiobjective Monte Carlo Tree Search for Real-Time Games

Multiobjective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multiobjective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a multiobjective Monte Carlo tree search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40 ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo tree search and a rolling horizon implementation of nondominated sorting evolutionary algorithm II (NSGA-II). Two different benchmarks are employed, deep sea treasure (DST) and the multiobjective physical traveling salesman problem (MO-PTSP). Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search)

Self-optimizing Machine Management

Today’s machine management systems in off-highway machines are designed to optimize with respect to a target function without integrating the entire machine or considering environmental interactions. For that reason the interdisciplinary project OCOM – “Organic Computing in Off-highway Machines” started in February 2009 to design an architecture for an off-highway machine in order to close that gap. Optimization of fuel consumption is exemplarily chosen even though many other goals are reachable. This paper will introduce the generic architecture; first results will be presented