Search results for: self-adaptive

2 Elitist Self-Adaptive Step-Size Search in Optimum Sizing of Steel Structures

Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

Abstract:

This paper covers application of an elitist selfadaptive

step-size search (ESASS) to optimum design of steel

skeletal structures. In the ESASS two approaches are considered for

improving the convergence accuracy as well as the computational

efficiency of the original technique namely the so called selfadaptive

step-size search (SASS). Firstly, an additional randomness

is incorporated into the sampling step of the technique to preserve

exploration capability of the algorithm during the optimization.

Moreover, an adaptive sampling scheme is introduced to improve the

quality of final solutions. Secondly, computational efficiency of the

technique is accelerated via avoiding unnecessary analyses during the

optimization process using an upper bound strategy. The numerical

results demonstrate the usefulness of the ESASS in the sizing

optimization problems of steel truss and frame structures.

Keywords: Structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames.}

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1 A Weighted Sum Technique for the Joint Optimization of Performance and Power Consumption in Data Centers

Authors: Samee Ullah Khan, C.Ardil

Abstract:

With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.

Keywords: Meta-heuristics, distributed systems, adaptive methods, resource allocation.

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