Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise

In single-objective optimization, it is well known that evolutionary algorithms also without further adjustments can tolerate a certain amount of noise in the evaluation of the objective function. In contrast, this question is not at all understood for multi-objective optimization. In this work, we conduct the first mathematical runtime analysis of a simple multi-objective evolutionary algorithm (MOEA) on a classic benchmark in the presence of noise in the objective functions. We prove that when bit-wise prior noise with rate $p \le \alpha/n$, $\alpha$ a suitable constant, is present, the \emph{simple evolutionary multi-objective optimizer} (SEMO) without any adjustments to cope with noise finds the Pareto front of the OneMinMax benchmark in time $O(n^2\log n)$, just as in the case without noise. Given that the problem here is to arrive at a population consisting of $n+1$ individuals witnessing the Pareto front, this is a surprisingly strong robustness to noise (comparably simple evolutionary algorithms cannot optimize the single-objective OneMax problem in polynomial time when $p = \omega(\log(n)/n)$). Our proofs suggest that the strong robustness of the MOEA stems from its implicit diversity mechanism designed to enable it to compute a population covering the whole Pareto front. Interestingly this result only holds when the objective value of a solution is determined only once and the algorithm from that point on works with this, possibly noisy, objective value. We prove that when all solutions are reevaluated in each iteration, then any noise rate $p = \omega(\log(n)/n^2)$ leads to a super-polynomial runtime. This is very different from single-objective optimization, where it is generally preferred to reevaluate solutions whenever their fitness is important and where examples are known such that not reevaluating solutions can lead to catastrophic performance losses.Comment: Appears at IJCAI 202

Dynamic QoS optimization architecture for cloud-based DDDAS

Cloud computing urges the need for novel on-demand approaches, where the Quality of Service (QoS) requirements of cloud-based services can dynamically and adaptively evolve at runtime as Service Level Agreement (SLA) and environment changes. Given the unpredictable, dynamic and on-demand nature of the cloud, it would be unrealistic to assume that optimal QoS can be achieved at design time. As a result, there is an increasing need for dynamic and self- adaptive QoS optimization solutions to respond to dynamic changes in SLA and the environment. In this context, we posit that the challenge of self-adaptive QoS optimization encompasses two dynamics, which are related to QoS sensitivity and conflicting objectives at runtime. We propose novel design of a dynamic data-driven architecture for optimizing QoS influenced by those dynamics. The architecture leverages on DDDAS primitives by employing distributed simulations and symbiotic feedback loops, to dynamically adapt decision making metaheuristics, which optimizes for QoS tradeoffs in cloud-based systems. We use a scenario to exemplify and evaluate the approach

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii

Enhancing sustainability in logistics through stochastic network routing mechanism optimization using ant colony algorithm

Stochastic networks are one of the most prevalent types of networks these days. Therefore, many researchers directed to study them and summarize the essential points and challenges they face in developing these types of networks, especially optimal route path selection. In this paper, a solution to this problem was addressed using the evolutionary algorithm ACO (Ant Colony Optimization), where the path with the lowest cost was obtained according to several scenarios studied in the research, which consider the fact that, the traffic information in the network is available either in a static or in a dynamic form in real-time. The proposed method presented contributions for real networks that can be used in many applications. The results are essential in solving the problem of choosing the optimal route. Also, they can be applied to various scenarios of the stochastic networks that exist in real life. Optimization improves logistics efficiency, which contributes to sustainability by minimizing fuel consumption, reducing emissions, and conserving resources