Full factorial experimental design for parameters selection of Harmony Search Algorithm

AbstractMetaheuristic may be defined as an iterative search process that intelligently performs the exploration and exploitationin the solution space aiming to efficiently find near optimal solutions. Various natural intelligences and inspirations have been artificially embedded into the iterative process. In this work, Harmony Search Algorithm (HSA), which is based on the melody fine tuning conducted by musicians for optimising the synchronisation of the music, was adopted to find optimal solutions of nine benchmarking non-linear continuous mathematical models including two-, three- and four-dimensions. Considering the solution space in a specified region, some models contained a global optimum and multi local optima. A series of computational experiments was used to systematically identify the best parameters of HSA and to compare its performance with other metaheuristics including the Shuffled Frog Leaping (SFL) and the Memetic Algorithm (MA) in terms of the mean and variance ofthe solutions obtained

An Application of the Harmony-Search Multi-Objective (HSMO) Optimization Algorithm for the Solution of Pump Scheduling Problem☆

Abstract In hydraulic systems, water is often pumped to reach higher elevations, so as to ensure the minimum required pressure and guarantee adequate service level. However, pumps cannot be instantly activated and people do not consume the resource in uniform mode throughout the day. To avoid direct pumping, water can be stored in tanks at a higher elevation, so that it can be supplied whenever there is a higher demand. Because of the significant costs required for pumping, energy-saving in water supply systems is one of the most challenging issues to ensure optimal management of water systems. Careful scheduling of pumping operations may lead not only to energy savings, but alsoto prevent damages, as consequence of reduction of operation times and switches. By means of computer simulation, an optimal schedule of pumps can be achieved using optimization algorithms. In this paper, a harmony-search multi-objective (HSMO) optimization approach is adapted to the pump scheduling problem. The model interfaces with the popular hydraulic solver, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the selected schedules. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to a case study, showing that the results are comparable with those of competitive meta-heuristic algorithms (e.g. Genetic Algorithms) and pointing out the suitability of the HSMO algorithm for pumping optimization

Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms

Sustainable Energy Portfolios for Small Island States

The study presents a cost effective electricity generation portfolio for six island states for a 20-year period (2015-2035). The underlying concept investigates whether adding sizeable power capacities of renewable energy sources (RES) options could decrease the overall costs and contribute to a more sustainable, indigenous electricity generation at the same time. Often, island states rely on fossil fuels which apart from dependence on foreign resources also includes an additional, significant transport cost. This is an extra motive to study the extent in which island states represent primary locations for RES technologies. For the aims of the present study an optimization model has been developed and following numerous runs the obtained results show that installing PV and battery capacities can delay-reduce the huge investments in fossil options in early periods. Thus, investment on RES can have a positive, long-term effect on the overall energy mix. This prompt development can happen without adding new subsidies but there is a need to address the existing socio-economic barriers with intelligent design of financing and economic instruments and capacity building as discussed in the conclusions.JRC.F.7-Renewables and Energy Efficienc

Recognizing faces prone to occlusions and common variations using optimal face subgraphs

An intuitive graph optimization face recognition approach called Harmony Search Oriented-EBGM (HSO-EBGM) inspired by the classical Elastic Bunch Graph Matching (EBGM) graphical model is proposed in this contribution. In the proposed HSO-EBGM, a recent evolutionary approach called harmony search optimization is tailored to automatically determine optimal facial landmarks. A novel notion of face subgraphs have been formulated with the aid of these automated landmarks that maximizes the similarity entailed by the subgraphs. For experimental evaluation, two sets of de facto databases (i.e., AR and Face Recognition Grand Challenge (FRGC) ver2.0) are used to validate and analyze the behavior of the proposed HSO-EBGM in terms of number of subgraphs, varying occlusion sizes, face images under controlled/ideal conditions, realistic partial occlusions, expression variations and varying illumination conditions. For a number of experiments, results justify that the HSO-EBGM shows improved recognition performance when compared to recent state-of-the-art face recognition approaches

Comparison Of HSRNAFold and RNAFold Algorithms for RNA Secondary Structure Prediction.

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance