Self-adjusting Scheme Research Articles

Self-Adjusting Evolutionary Algorithms for Multimodal Optimization

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functions which do not require the algorithm to flip several bits simultaneously to make progress. In fact, existing self-adjusting algorithms are not designed to detect local optima and do not have any obvious benefit to cross large Hamming gaps. We suggest a mechanism called stagnation detection that can be added as a module to existing evolutionary algorithms (both with and without prior self-adjusting schemes). Added to a simple (1+1) EA, we prove an expected runtime on the well-known Jump benchmark that corresponds to an asymptotically optimal parameter setting and outperforms other mechanisms for multimodal optimization like heavy-tailed mutation. We also investigate the module in the context of a self-adjusting (1+\(\lambda \)) EA. To explore the limitations of the approach, we additionally present an example where both self-adjusting mechanisms, including stagnation detection, do not help to find a beneficial setting of the mutation rate. Finally, we investigate our module for stagnation detection experimentally.

Runtime Analysis for Self-adaptive Mutation Rates

We propose and analyze a self-adaptive version of the $$(1,\lambda )$$ evolutionary algorithm in which the current mutation rate is encoded within the individual and thus also subject to mutation. A rigorous runtime analysis on the OneMax benchmark function reveals that a simple local mutation scheme for the rate leads to an expected optimization time (number of fitness evaluations) of $$O(n\lambda /\log \lambda +n\log n)$$ when $$\lambda$$ is at least $$C \ln n$$ for some constant $$C > 0$$ . For all values of $$\lambda \ge C \ln n$$ , this performance is asymptotically best possible among all $$\lambda$$ -parallel mutation-based unbiased black-box algorithms. Our result rigorously proves for the first time that self-adaptation in evolutionary computation can find complex optimal parameter settings on the fly. In particular, it gives asymptotically the same performance as the relatively complicated self-adjusting scheme for the mutation rate proposed by Doerr, Giesen, Witt, and Yang (Algorithmica 2019). On the technical side, the paper contributes new tools for the analysis of two-dimensional drift processes arising in the analysis of dynamic parameter choices in EAs, including bounds on occupation probabilities in processes with non-constant drift.

Automation and Combination of Linear-Programming Based Stabilization Techniques in Column Generation

The convergence of a column generation algorithm can be improved in practice by using stabilization techniques. Smoothing and proximal methods based on penalizing the deviation from the incumbent dual solution have become standards of the domain. Interpreting column generation as cutting plane strategies in the dual problem, we analyze the mechanisms on which stabilization relies. In particular, the link is established between smoothing and in-out separation strategies to derive generic convergence properties. For penalty function methods as well as for smoothing, we describe proposals for parameter self-adjusting schemes. Such schemes make initial parameter tuning less of an issue as corrections are made dynamically. Such adjustments also allow to adapt the parameters to the phase of the algorithm. We provide extensive test reports that validate our self-adjusting parameter scheme and highlight their performances. Our results also show that using smoothing in combination with penalty function yields a cumulative effect on convergence speed-ups.

An intelligent self-adjusting sensor for smart home services based on ZigBee communications

Wireless sensor networks (WSNs) have been becoming increasingly essential in recent years because of their ability to manage real-time situational information for various novel services. Recently, the scope of WSN technologies has been expanded to places such as the home, in order to provide the residents with various intelligent services, such as home automation services or home energy management services. However, due to their architectural constraints, such as the trade-off between the performance and cost, WSNs are not effectively implemented in home environments. Therefore, this paper proposes a ZigBee-based intelligent self-adjusting sensor (ZiSAS) in order to address these concerns. This paper presents a situation-based selfadjusting scheme, an event-based self-adjusting sensor network, and hardware and middleware implementation. We also introduce some smart home services using the proposed system. We implemented our system in real test bed and conducted an experiment. Our experiment shows that we reduce the system's energy consumption.

Self-adjusting routing schemes for time-varying traffic in scale-free networks

We consider the effects of time-varying packet generation rates in the performance of communication networks. The time variations could be a result of the patterns in human activities. As a model, we study the effects of a degree-dependent packet generation rate that includes a sinusoidal term. Applying a modified traffic awareness protocol (TAP) previously proposed for static packet generation rates to the present situation leads to an altered value of the optimization parameter, when compared to that obtained in the static case. To enhance the performance and to cope with the time-varying effects better, we propose a class of self-adjusting traffic awareness protocols that makes use of instantaneous traffic information beyond that included in the modified TAP. Two special cases that make use of global and local information, respectively, are studied. Comparing results of our proposal schemes with the modified TAP, it is shown that the present self-adjusting schemes perform more effectively.

Analysis and evaluation of a multiple gateway traffic-distribution scheme for gateway clusters

Next-generation Internet gateways are expected to deal with higher volume of network traffic and also perform more sophisticated tasks besides packet forwarding. As the scale-up approach does not escape from the tradeoff between functionality and performance, architectural improvements such as clustering become necessary in the design of future Internet gateways. In this paper, we investigate different clustering architectures for high-performance, feature-rich Internet gateways and formally define the optimization problem behind these architectures as Multiple Gateway Traffic-Distribution Problem, both in a discrete and a continuous form. In addition to proposing various algorithms that solve the problem exactly and approximately, we also develop an on-line, self-adjusting scheme based on the solution algorithms. The numerical results of simulation suggest that the proposed approximate solution algorithms are effective and efficient, and the derived adaptive scheme is able to make the best decision on traffic distribution when dealing with the dynamic nature of network traffic in practice.

A self-adjusting quality of service control scheme

We propose and analyze a self-adjusting Quality of Service (QoS) control scheme with the goal of optimizing the system reward as a result of servicing different priority clients with varying workload, QoS and reward/penalty requirements. Our scheme is based on resource partitioning and designated “degrade QoS areas” such that system resources are partitioned into priority areas each of which is reserved specifically to serve only clients in a corresponding class with no QoS degradation, plus one “degraded QoS area” into which all clients can be admitted with QoS adjustment being applied only to the lowest priority clients. We show that the best partition is dictated by the workload and the reward/penalty characteristics of clients in difference priority classes. The analysis results can be used by a QoS manager to optimize the system total reward dynamically in response to changing workloads at run time. We demonstrate the validity of our scheme by means of simulation and comparing the proposed QoS self-adjusting scheme with those that do not use resource partitioning or designated degraded QoS areas.

Self-adjusting compensating thermal lens to balance the thermally induced lens in solid-state lasers

The thermal lens is a critical issue, particularly in high-power diode-pumped solid-state laser rods. A self-adjusting scheme for compensation of the thermally induced lens is presented. The requirements for such an element and its influence on the resonator are discussed. With an appropriate compensating element and a suitable resonator design, constant beam parameters are expected to be achieved over a pump range of several kilowatts.

A distributed shared memory system with self-adjusting coherence scheme

The performance of distributed shared memory depends on the memory coherence algorithms and the access characteristics of shared data. In this paper, we propose an efficient coherence scheme using multiple coherence algorithms with self-adjusting feature. Our method can dynamically choose a more adaptive coherence algorithm for each variable class and the incorrect classification of shared variables will not affect the performance. We show that for each fixed classification, application programs suffer 5.1%, 4.6%, and 48.9% increases in the average execution time, when compared against the performance of a self-adjusting scheme. Experiments have shown our approach achieving good performance.

Self-adjusting Mapping: A Heuristic Mapping Algorithm for Mapping Parallel Programs on to Transputer Networks

The problem of mapping parallel programs on to multiprocessor systems is a fundamental problem of great significance in parallel processing. In this paper we propose a fast heuristic algorithm to solve this problem on transputer networks. Our mapping algorithm mainly contains three modules: grouping, placement and routeing, where grouping puts processes in the program into tasks which can be one-to-one placed on to processors in the transputer network, placement sets the grouped tasks on to the processors and routeing produces edge-disjoint physical communication paths for logical communication requirements. The algorithm works by combining three modules under a self-adjusting scheme towards a successful mapping result. For mapping n processes in an arbitrary parallel program on to m processors in a transputer network of grid structure, our algorithm has a worst-case time complexity O(max {n 1 , m 5 }) under full adjusting, O(max {n 2 , m 4 }) under semi-adjusting and O(max {n 2 , m 2 }) under no adjusting, where the last holds only for the transputer networks providing message routeing and multiplexing. The algorithm has been implemented in Occam on the Hathi-2 transputer system.

Decentralized Dynamic Control of Manipulation Robots

An approach to the synthesis of decentralized self-adjusting controller for manipulation robots is given in the paper. This controller is intended for serving trajectories defined in the space of manipulator generalized coordinates. The synthesis is carried out on a basis of a quadratic performance index. The high speed of controller adaptation to the parameter deviations is achieved due to the open-loop self-adjusting scheme. On-line algorithms for the identification of the payload mass, effective moments of inertia and damping coefficients reduced to the output shafts of gear trains of actuators are presented. Also the computational scheme for gravitational loads which is used for the control purposes is given in the paper. Simulation results are presented and discussed.

Implementation of a self-adjusting scheme in queueing systems

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Implementation of a self-adjusting scheme in queueing systems

Implementation of a self-adjusting scheme in queueing systems