12,579 research outputs found
The Novel Approach of Adaptive Twin Probability for Genetic Algorithm
The performance of GA is measured and analyzed in terms of its performance
parameters against variations in its genetic operators and associated
parameters. Since last four decades huge numbers of researchers have been
working on the performance of GA and its enhancement. This earlier research
work on analyzing the performance of GA enforces the need to further
investigate the exploration and exploitation characteristics and observe its
impact on the behavior and overall performance of GA. This paper introduces the
novel approach of adaptive twin probability associated with the advanced twin
operator that enhances the performance of GA. The design of the advanced twin
operator is extrapolated from the twin offspring birth due to single ovulation
in natural genetic systems as mentioned in the earlier works. The twin
probability of this operator is adaptively varied based on the fitness of best
individual thereby relieving the GA user from statically defining its value.
This novel approach of adaptive twin probability is experimented and tested on
the standard benchmark optimization test functions. The experimental results
show the increased accuracy in terms of the best individual and reduced
convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer
Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201
Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level
Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy
Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676
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