Hybrid Harmony Search Algorithm Research Articles

Key design of driving industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks

In the Industry 4.0 framework based on IoT and smart manufacturing, it is essential to support factory automation and flexibility in harsh or dynamic industrial environments. State-of-the-art technology suggests building a controlled workspace using large-scale deployment of wireless sensors. To overcome the technological challenges in scalability and heterogeneity for large-scale industrial deployment, group-based industrial wireless sensor networks (GIWSNs) are suggested, in which wireless sensors are divided into multiple groups for multiple monitoring tasks, and each group of sensors is deployed densely within a subarea in a large plant or along a long production/assembly line, while connectivity between groups is required. As wireless sensors are equipped with batteries with limited power, it has been challenging to plan sleep schedules of sensors, which are influenced significantly by deployment of such a large-scale GIWSN. However, most previous works on wireless sensor networks independently investigated deployment and sleep scheduling problems, both of which have been shown to be NP-hard. Therefore, this work jointly considers deployment and sleep scheduling of sensors in a GIWSN along a production line. Via the theory of symmetries, we alleviate the computational concerns from multiple groups to one group and another medium-size group. Then we propose a hybrid harmony search and genetic algorithm, which incorporates deployment and sleep schedules to reduce energy consumption. Simulations verify this joint methodology to effectively achieve energy efficiency.

A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints

With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR, the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm.

A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure

This paper presents a new variant of the Harmony Search (HS) algorithm. This Hybrid Harmony Search (HHS) algorithm follows a new approach to improvisation: while retaining HS algorithm Harmony Memory and pitch adjustment functions, it replaces the HS algorithm randomization function with Global-best Particle Swarm Optimization (PSO) search and neighbourhood search. HHS algorithm performance is tested on six discrete truss structure optimization problems under multiple loading conditions. Optimization results demonstrate the excellent performance of the HHS algorithm in terms of both optimum solution and the convergence behaviour in comparison with various alternative optimization methods.

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Abstract Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial (NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique

Abstract This paper attempts to minimize power losses in radial distribution networks and facilitates an enhancement in bus voltage profile by determining optimal locations, optimally sized distributed generators and shunt capacitors by hybrid Harmony Search Algorithm approach. To overcome the drawback of premature and slow convergence of Harmony Search Algorithm (HSA) over multi model fitness landscape, the Particle Artificial Bee Colony algorithm (PABC) is utilized to enhance the harmony memory vector. In the first approach, the formulation echoes the determination of loss sensitivity factor to decide the sensitive nodes and thereafter decides on the optimal rating through the use of hybrid Algorithm. The second approach encircles the role of hybrid Algorithm to search for both the optimal candidate nodes and sizing of compensating devices by significant increase in loss reduction with the former approach. The procedure travels to examine the robustness of the proposed hybrid approach on 33 and 119 node test systems and the result outcomes are compared with the other techniques existing in the literature. The simulation results reveal the efficiency of the proposed hybrid algorithm in obtaining optimal solution for simultaneous placement of distributed generators and shunt capacitors in distribution networks.

Hybrid Harmony Search Algorithms

Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

Hybridization of harmony search with hill climbing for highly constrained nurse rostering problem

This paper proposes a hybrid harmony search algorithm (HHSA) for solving the highly constrained nurse rostering problem (NRP). The NRP is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses; each has specific skills and work contract, to a predefined rostering period according to a set of constraints. The harmony search is a metaheuristic approach, where the metaheuristics are the most successful methods for tackling this problem. In HHSA, the harmony search algorithm is hybridized with the hill climbing optimizer to empower its exploitation capability. Furthermore, the memory consideration operator of the HHSA is modified by replacing the random selection scheme with the global-best concept of particle swarm optimization to accelerate its convergence rate. The standard dataset published in the first international nurse rostering competition 2010 (INRC2010) was utilized to evaluate the proposed HHSA. Several convergence scenarios have been employed to study the effects of the two HHSA modifications. Finally, a comparative evaluation against twelve other methods that worked on the INRC2010 dataset is carried out. The experimental results show that the proposed method achieved five new best results, and 33 best published results out of 69 instances as achieved by other comparative methods.

A methodology for optimal operation of pumping stations in urban drainage systems

Over the past two decades, flood risks have presented a significant challenge for urban areas owing to the increased peak flows resulting from urbanization and climate change. In metropolitan areas, large-scale networks of sewer pipes and pump stations are the main facilities used to mitigate flood damage. During flooding periods, when drainage gates are closed, pumping operations play a major role in efficiently reducing flood damage. To obtain an optimal policy for these operations, a novel robust approach is presented here. In this approach, a long-term operating rule is designed by coupling a mathematical model and a new hybrid harmony search algorithm, while considering the stochastic nature of rainfall events. Application of the proposed method to a real urban drainage system showed a high efficiency in terms of flood mitigation and performance of pumps compared to the current operating rule for the pump station. Compared to the traditional approach, optimal operation decreased peak water levels by an average of 40%, without increasing the number of pump switches. Based on these results, optimizing pumping operations appear to be a practical and highly effective way to reduce flood water levels and urban inundation without making changes to the actual infrastructure of the system.

Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market

A hybrid technique for solving the congestion management problem in an electricity market based on harmony search algorithm (HAS) and Fuzzy mechanism is presented in this article. This algorithm does not require initial value setting for the variables and does not require differential gradients, thus it can consider discontinuous functions as well as continuous functions. The HAS is a recently developed powerful evolutionary algorithm, inspired by the improvisation process of musicians, for solving single/multiobjective optimization problems. In the proposed technique, each musician plays a note for finding a best harmony all together. Transmission pricing and congestion management are the key elements of a competitive electricity market based on direct access. They also focus of much of the debate concerning alternative approaches to the market design and the implementation of a common carrier electricity system. This article focuses on the tradeoffs between simplicity and economic efficiency in meeting the objectives of a transmission pricing and congestion management scheme. The effectiveness of the proposed technique is applied on 30 and 118 bus IEEE standard power system in comparison with CPSO, PSO‐TVAC, and PSO‐TVIW. The numerical results demonstrate that the proposed technique is better and superior than other compared methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 90–98, 2016

Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges

This paper aims to find sustainable designs of post-tensioned concrete box-girder pedestrian bridges. A hybrid harmony search algorithm combining threshold optimization is used to find the geometry and the materials for which the sum of the costs or the emissions are the lowest, yet satisfying the requirements for structural safety and durability. An experimental design method was used to adjust the algorithm parameters. The parametric study was applied to three-span deck bridges ranging from 90m to 130m. The findings indicated that both objectives lead to similar cost results. However, the variables presented some differences. Such deviations suggested greater depths, more strands and a lower concrete strength for CO2 target functions. Carbonation captured less than 1% of the deck emissions over 100years. This methodology leads to a precise analysis of the practical rules to achieve an environmental design approach.

Application of multi‐objective evolutionary algorithms for the rehabilitation of storm sewer pipe networks

AbstractIn recent decades, evolutionary optimisation algorithms have been used successfully for a wide variety of water resources engineering problems and their applications are still increasing. In this research work, a hybrid harmony search algorithm, ‘Non‐dominated Sorting Harmony Search’ algorithm is developed and compared with two state‐of‐the‐art multi‐objective evolutionary algorithms – the non‐dominated sorting genetic algorithm (NSGA)‐II and multi‐objective particle swarm optimisation (MOPSO) algorithms – for assigning optimal rehabilitation plans for sewer pipe networks. The algorithms considered were validated using some standard test functions reported in the literature and compared with each other in terms of several metrics. These algorithms were then linked to the SWMM‐EPA hydraulic model and applied to a storm sewer pipe network case study in Seoul, South Korea, to obtain the best rehabilitation plans for pipe replacements. The results showed that the algorithms considered have different behaviours in solving the benchmark tests and rehabilitation problem. The proposed hybrid multi‐objective harmony search algorithm provides better optimal solutions in terms of different metrics and clearly outperforms the other two algorithms for the rehabilitation of the storm sewer pipe networks.

Hybrid Harmony Search Algorithm for Global Optimization Problems

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

For the purpose of enhancing the search ability of the cuckoo search (CS) algorithm, an improved robust approach, called HS/CS, is put forward to address the optimization problems. In HS/CS method, the pitch adjustment operation in harmony search (HS) that can be considered as a mutation operator is added to the process of the cuckoo updating so as to speed up convergence. Several benchmarks are applied to verify the proposed method and it is demonstrated that, in most cases, HS/CS performs better than the standard CS and other comparative methods. The parameters used in HS/CS are also investigated by various simulations.

Hybrid Harmony Search Algorithm and Interior Point Method for Economic Dispatch with Valve-Point Effect

Abstract This paper proposes a new hybrid algorithm combining harmony search (HS) algorithm and interior point method (IPM) for economic dispatch (ED) problem with valve-point effect. ED problem with valve-point effect is modeled as a non-linear, constrained and non-convex optimization problem having several local minima. IPM is a best non-linear optimization method for convex optimization problems. Since ED problem with valve-point effect has multiple local minima, IPM results in a local optimum solution. In order to avoid IPM getting trapped in a local optimum, a new evolutionary algorithm HS, which is good in global exploration, has been combined. In the hybrid method, HS is used for global search and IPM for local search. The hybrid method has been tested on three different test systems to prove its effectiveness. Finally, the simulation results are also compared with other methods reported in the literature.

Online Order Priority Evaluation Based on Hybrid Harmony Search Algorithm of Optimized Support Vector Machines

To make production plan, online order priority evaluation is the current priority weakness of online order evaluation model. This thesis proposes an online order priority evaluation model based on hybrid harmony search algorithm of optimized support vector machine (HHS-SVM). Firstly, an online order priority evaluation index system is build, and then support vector machine is adopted to build an online order priority evaluation model; secondly, harmony search algorithm is used to optimize the parameters of support vector machine; what's more, in the parameter optimization process the foraging behavior of AFSA is introduced to improve the ability and convergence speed of algorithm escaping from the local optimal solution; finally, simulation test is used to test the performance of the model. The simulation results show that HHS-SVM improves the accuracy of the online order priority evaluation relative to the comparison model. What's more, the online order priority evaluation model is feasible and effective.

Closure to “Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using a Hybrid Harmony Search Algorithm” by Halil Karahan, Gurhan Gurarslan, and Zong Woo Geem

“Quantitative assessment of climate change and human impacts on long-term hydrologic response: A case study in sub-basin of the Yellow River, China.” Int. J. Climatol., 30(14), 2130–2137. Wang, W., et al. (2012). “Reference evapotranspiration change and the causes across the YellowRiver Basin during 1957–2008 and their spatial and seasonal differences.” Water Resour. Res., 48(5), W05530. Zhang, S., and Lu, X. (2009). “Hydrological responses to precipitation variation and diverse human activities in a mountainous tributary of the lower Xijiang, China.” Catena, 77(2), 130–142. Zhang, X., Zhang, L., Zhao, J., Rustomji, P., and Hairsine, P. (2008). “Responses of streamflow to changes in climate and land use/cover in the Loess Plateau, China.” Water Resour. Res., 44(7), W00A07.

Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Harmony Search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Mutation is a vital component used in Evolutionary Algorithms (EA) where a value in the population is randomly selected to be altered to improve the evolution process. The original HS algorithm applies an operation similar to mutation during the random consideration operator. During random selection operator a value within the range of the decision variable is selected randomly to explore different areas in the search space. This paper aims at experimentally evaluating the performance of HS algorithm after replacing the random consideration operator in the original HS with five different mutation methods. The different variations of HS are experimented on standard benchmark functions in terms of final obtained solution and convergence speed. The results show that using polynomial mutation improves the performance of the HS algorithm for most of the used functions.

Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimics the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA have been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experimental results of the proposed algorithm are compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.

A hybrid harmony search algorithm for the flexible job shop scheduling problem

In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize makespan. First of all, to make the harmony search (HS) algorithm adaptive to the FJSP, the converting techniques are developed to convert the continuous harmony vector to a kind of discrete two-vector code for the FJSP. Secondly, the harmony vector is mapped into a feasible active schedule through effectively decoding the transformed two-vector code, which could largely reduce the search space. Thirdly, a resultful initialization scheme combining heuristic and random strategies is introduced to make the initial harmony memory (HM) occur with certain quality and diversity. Furthermore, a local search procedure is embedded in the HS algorithm to enhance the local exploitation ability, whereas HS is employed to perform exploration by evolving harmony vectors in the HM. To speed up the local search process, the improved neighborhood structure based on common critical operations is presented in detail. Empirical results on various benchmark instances validate the effectiveness and efficiency of our proposed algorithm. Our work also indicates that a well designed HS-based method is a competitive alternative for addressing the FJSP.