The effects of altered heat stress on voluntary pacing strategies during prolonged cycling

Central Governor Model (CGM) suggests that perturbations in the rate of heat storage (AS) are centrally integrated to regulate exercise intensity in a feed-forward fashion to prevent excessive thermal strain. We directly tested the CGM by manipulating ambient temperature (Tam) at 20-minute intervals from 20°C to 35°C, and returning to 20°C, while cycling at a set rate of perceived exertion (RPE). The synchronicity of power output (PO) with changes in HS and Tam were quantified using Auto-Regressive Integrated Moving Averages analysis. PO fluctuated irregularly but was not significantly correlated to changes in thermo physiological status. Repeated measures indicated no changes in lactate accumulation. In conclusion, real time dynamic sensation of Tam and integration of HS does not directly influence voluntary pacing strategies during sub-maximal cycling at a constant RPE while non-significant changes in blood lactate suggest an absence of peripheral fatigue.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.