Martin Lukasiewycz

Opt4J: a modular framework for meta-heuristic optimization

This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.

Efficient symbolic multi-objective design space exploration

Nowadays many design space exploration tools are based on Multi-Objective Evolutionary Algorithms (MOEAs). Beside the advantages of MOEAs, there is one important drawback as MOEAs might fail in design spaces containing only a few feasible solutions or as they are often afflicted with premature convergence, i.e., the same design points are revisited again and again. Exact methods, especially Pseudo Boolean solvers (PB solvers) seem to be a solution. However, as typical design spaces are multi-objective, there is a need for multi-objective PB solvers. In this paper, we will formalize the problem of design space exploration as multi-objective 0-1 ILP. We will propose (1) a heuristic approach based on PB solvers and (2) a complete multi-objective PB solver based on a backtracking algorithm that incorporates the non-dominance relation from multi-objective optimization and is restricted to linear objective functions. First results from applying our novel multi-objective PB solver to synthetic problems will show its effectiveness in small sized design spaces as well as in large design spaces only containing a few feasible solutions. For non-linear and large problems, the proposed heuristic approach is outperforming common MOEA approaches. Finally, a real world example from the automotive area will emphasize the efficiency of the proposed algorithms.

SAT-decoding in evolutionary algorithms for discrete constrained optimization problems

For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver sufficiently good solutions in an acceptable time. However, for discrete problems that are restricted by several constraints it is mostly a hard problem to even find a single feasible solution. In these cases, the optimization heuristics typically perform poorly as they mainly focus on searching feasible solutions rather than optimizing the objectives. In this paper, we propose a novel methodology to obtain feasible solutions from constrained discrete problems in population- based optimization heuristics. At this juncture, the constraints have to be converted into the Prepositional Satisfiability Problem (SAT). Obtaining a feasible solution is done by the DPLL algorithm which is the core of most modern SAT solvers. It is shown in detail how this methodology is implemented in Multi-objective Evolutionary Algorithms. The SAT solver is used to obtain feasible solutions from the genetic encoded information on arbitrarily hard solvable problems where common methods like penalty functions or repair strategies are failing. Handmade test cases are used to compare various configurations of the SAT solver. On an industrial example, the proposed methodology is compared to common strategies which are used to obtain feasible solutions.

Symbolic reliability analysis and optimization of ECU networks

Increasing reliability at a minimum amount of extra cost is a major challenge in todays ECU network design. Considering reliability as an objective already in early design phases has the potential to avoid expensive modifications in later design phases. Hence, there is a need for an appropriate optimization process and efficient analysis techniques to evaluate the found implementations. In this paper, we will show how symbolic techniques can be used to efficiently analyze and optimize such reliable systems. The contribution of this paper is (1) a symbolic reliability analysis that makes use of a partitioned structure function and (2) a symbolic optimization process based on binary ILP solvers. Our case study from the automotive area will show a significant speed-up using our analysis technique. Moreover, our optimization approach is able to offer implementations with considerably improved reliability at no additional costs as well as implementations with reduced costs without decreasing their reliability.

Holistic Scheduling of Real-Time Applications in Time-Triggered In-Vehicle Networks

As time-triggered communication protocols [e.g., time-triggered controller area network (TTCAN), time-triggered protocol (TTP), and FlexRay] are widely used on vehicles, the scheduling of tasks and messages on in-vehicle networks becomes a critical issue for offering quality-of-service (QoS) guarantees to time-critical applications on vehicles. This paper studies a holistic scheduling problem for handling real-time applications in time-triggered in-vehicle networks where practical aspects in system design and integration are captured. The contributions of this paper are multifold. First, it designs a novel scheduling algorithm, referred to as Unfixed Start Time (UST) algorithm, which schedules tasks and messages in a flexible way to enhance schedulability. In addition, to tolerate assignment conflicts and further improve schedulability, it proposes two rescheduling and backtracking methods, namely, Rescheduling with Offset Modification (ROM) and Backtracking and Priority Promotion (BPP) procedures. Extensive performance evaluation studies are conducted to quantify the performance of the proposed algorithm under a variety of scenarios.

Symbolic system synthesis in the presence of stringent real-time constraints

Stringent real-time constraints lead to complex search spaces containing only very few or even no valid implementations. Hence, while searching for a valid implementation a substantial amount of time is spent on timing analysis during system synthesis. This paper presents a novel system synthesis approach that efficiently prunes the search space in case real-time constraints are violated. For this purpose, the reason for a constraint violation is analyzed and a deduced encoding removes it permanently from the search space. Thus, the approach is capable of proving both the presence and absence of a correct implementation. The key benefit of the proposed approach stems from its integral support for real-time constraint checking. Its efficiency, however, results from the power of deduction techniques of state-of-the-art Boolean Satisfiability (SAT) solvers. Using a case study from the automotive domain, experiments show that the proposed system synthesis approach is able to find valid implementations where former approaches fail. Moreover, it is up to two orders of magnitude faster compared to a state-of-the-art approach.

Switched FlexRay: Increasing the effective bandwidth and safety of FlexRay networks

With the continued demand for more and innovative functions in series automobiles, significantly higher data-rates and more reliable communication are necessary than traditional automotive bus systems, e.g., the controller area network (CAN), provide. In this paper, we present a novel hardware device for FlexRay networks, which splits the bus into separate branches and operates as a selective central switch.

Multi-objective routing and topology optimization in networked embedded systems

Modern networked embedded system design has to cope with multiple design objectives. One major challenge is the determination of optimal routings with respect to these objectives. Existing automatic optimization approaches carry out a two step optimization: First, they perform a multi-objective topology optimization of the networked embedded system. Then, a multi-objective routing optimization for a subset of Pareto optimal solutions obtained from the first step is performed. In general, this may exclude several globally optimal solutions from the optimization process. To overcome this drawback, a unified approach based on multi-objective evolutionary algorithms is presented that ensures a combined optimization of the topology and routing. Since the system topology is varied within the optimization, the main contribution of this paper contribution is a novel routing technique that always samples feasible paths using a topology independent genetic encoding. This encoding preserves optimized routing information when changing the underlying topology. An experimental evaluation shows the effectiveness of the presented approach.

Solving multi-objective pseudo-boolean problems

Integer Linear Programs are widely used in areas such as routing problems, scheduling analysis and optimization, logic synthesis, and partitioning problems. As many of these problems have a Boolean nature, i.e., the variables are restricted to 0 and 1, so called Pseudo-Boolean solvers have been proposed. They are mostly based on SAT solvers which took continuous improvements over the past years. However, Pseudo-Boolean solvers are only able to optimize a single linear function while fulfilling several constraints. Unfortunately many real-world optimization problems have multiple objective functions which are often conflicting and have to be optimized simultaneously, resulting in general in a set of optimal solutions. As a consequence, a single-objective Pseudo-Boolean solver will not be able to find this set of optimal solutions. As a remedy, we propose three different algorithms for solving multi-objective Pseudo-Boolean problems. Our experimental results will show the applicability of these algorithms on the basis of several test cases.

Symbolic Reliability Analysis of Self-healing Networked Embedded Systems

In recent years, several network online algorithms have been studied that exhibit self-x properties such as self-healing or self-adaption. These properties are used to improve systems characteristics like, e.g., fault-tolerance, reliability, or load-balancing. In this paper, a symbolic reliability analysis of self-healing networked embedded systems that rely on self-reconfiguration and self-routing is presented. The proposed analysis technique respects resource constraints such as the maximum computational load or the maximum memory size, and calculates the achievable reliability of a given system. This analytical approach considers the topology of the system, the properties of the resources, and the executed applications. Moreover, it is independent of the used online algorithms that implement the self-healing properties, but determines the achievable upper bound for the systems reliability. Since this analysis is not tailored to a specific online algorithm, it allows a reasonable decision making on the used algorithm by enabling a rating of different self-healing strategies. Experimental results show the effectiveness of the introduced technique even for large networked embedded systems.