Memory Consideration Research Articles

Island neighboring heuristics harmony search algorithm for flow shop scheduling with blocking

• Island Neighboring Heuristics Harmony Search (INHS) for Flow Shop Scheduling is proposed. • Island model concept utilized in INHS to enhance solutions diversity. • Three neighboring heuristics are utilized in INHS to improve the local exploitation. • Two selection memory consideration methods are utilized: roulette-wheel and global-best. • Constraints are maintained during the search through repair strategy. This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to tackle the blocking flow-shop scheduling problem. The island model is used to diversify the population and thus enhance the algorithm performance. The proposed method distributes the individuals in the population into different islands or sub-population. Then the harmony search algorithm iterates to look for and to develop a new solution enhanced by using neighboring heuristics. A migration process is applied, after a predefined number of iterations, to perform an exchange between some individuals in islands. The proposed algorithm is evaluated using 12 real-world datasets, each with 10 instances. A sensitivity analysis of the island model parameters is conducted to choose values that minimize the total flow time. The evaluation is conducted using two criteria, the number of evolutions and the Central Processing Unit (CPU) time using six comparative methods and sixteen comparative methods, respectively. For the first criterion, the proposed algorithm excels other comparative algorithms when solving instances of four datasets. For the second criterion, the proposed algorithm outperforms other comparative methods in six out of the twelve datasets. In conclusion, The obtained results prove the efficiency and competitiveness of the proposed algorithm when tackling the blocking flow-shop scheduling problem.

A Fully-Self-Adaptive Harmony Search GMDH-Type Neural Network Algorithm to Estimate Shear-Wave Velocity in Porous Media

Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical parameters, and fracture assessment. However, measuring this important parameter is either impossible or difficult due to the challenges related to horizontal and deviation wells or the difficulty in reaching cores. Artificial Intelligence (AI) techniques, especially Machine Learning (ML), have emerged as efficient approaches for dealing with such challenges. Therefore, considering the advantage of the ML, the current research proposes a novel Fully-Self-Adaptive Harmony Search—Group Method of Data Handling (GMDH)-type neural network, named FSHS-GMDH, to estimate the VS parameter. In this way, the Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR) parameters are calculated automatically. A novel method is also introduced to adjust the value of the Bandwidth (BW) parameter based on the cosine wave and each decision variable values. In addition, a variable-size harmony memory is proposed to enhance both the diversification and intensification. Our proposed FSHS-GMDH algorithm quickly explores the problem space and exploits the best regions at the late iterations. This algorithm allows for the training of the prediction model based on the P-wave velocity (VP) and the bulk density of rock (RHOB). Applying the proposed algorithm to a carbonate petroleum reservoir in the Persian Gulf demonstrates that it is capable of accurately estimating the VS parameter better than state-of-the-art machine learning methods in terms of the coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE).

Double bit range estimation with eight estimators for CABAC in VVC

This work describes the modification of Context-based Adaptive Binary Arithmetic Coding (CABAC) using the double bit range estimation in the VVC engine and the consideration of range updates by using eight hypothetical probability estimators. The focus is on the selected adaptation rates performed in these proposed estimators, which are chosen based on memory consideration and coding efficiency. An investigation of arithmetic coding engines with multi-hypothesis probability estimates and their consideration of contextual modeling of entropy coding at the level of transform coefficients. The proposed scheme enables a quantitative representation of probabilistic predictions linearly and describes the scalability potential for higher accuracy. In addition, this work discusses the hardware implementation, which is based on simple operations such as bitwise operations and subinterval updates. The experimental results validate the effectiveness of the proposed approach specified in VTM framework. The improved results show that it provides more significant gains in terms of RA and LD, which is better than the AI configuration.

Optimization of Tuned Liquid Damper Including Different Liquids for Lateral Displacement Control of Single and Multi-Story Structures

This study focuses on tuned liquid dampers (TLDs) using liquids with different characteristics optimized with the adaptive harmony search algorithm (AHS). TLDs utilize the characteristic features of the liquid to absorb the dynamic forces entering the structure and benefit from the sloshing movement and the spring stiffness created by the liquid mass. TLDs have been optimized to investigate the effect of liquid characteristics on the control by analyzing various liquids. For optimization, the memory consideration ratio (HMCR) and fret width (FW) values were adapted from the classical harmony search (HS) algorithm parameters. The TLDs were used on three types of structure models, such as single-story, 10, and 40 stories. The contribution of the liquid characteristics to the damping performance was investigated by optimizing the minimum displacement under seismic excitation. According to the results, it was understood that the liquid density and kinematic viscosity do not affect single-story structures alone. However, two characteristic features should be evaluated together. As the structure mass increases, the viscosity and density become more prominent.

Adaptive Harmony Search for Tuned Liquid Damper Optimization under Seismic Excitation

In this study, the tuned liquid damper (TLD) device was optimized by the harmony search (HS) and adaptive harmony search algorithms (AHS). Using the harmony search algorithm, seismic excitations were directed at single and ten-story structures, and TLD parameters were optimized to minimize building movement. To improve design parameters, the optimization process was repeated by adapting the design factors of the harmony search algorithm. For this purpose, both the harmony memory consideration ratio (HMCR) and fret width (FW) were gradually reduced by providing an initial value, and optimum algorithm parameters were obtained. As a result of both optimizations, in a critical seismic analysis, the displacements of the adaptive harmony search showed smaller means and standard deviations than those of the classical harmony search.

A Reward Population-Based Differential Genetic Harmony Search Algorithm

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm

The query optimiser is a vital part of any distributed database mechanism. Reducing the execution period of the query depends on reaching an ideal query execution plan. Due to this issue's NP-hard nature, a hybrid harmony search and an artificial bee colony algorithm can be useful. The harmony is used to call query plans and signify them by S-dimension real vectors. A harmony memory is a place for creating and storing a primary population of harmony vectors. Then, bees explore harmony memory as a food source. The production of a novel nominate harmony out of all query plans in the harmony memory requires a pitch adjustment principle, a memory consideration one, and a random re-initialisation. Lastly, the new candidate vector replaces the worst harmony vector when it works better. The simulation outcomes have indicated that the introduced method reduces the expenses of evaluating a query compared to the harmony search and bee colony optimisation algorithms. However, this method has a longer execution time.

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Based on the non-permutation property of intercell scheduling in flowline manufacturing cells, a hybrid harmony search algorithm is proposed to solve the problem with the makespan criterion. On the basis of the basic harmony search algorithm, the three key elements of memory consideration, pitch adjustment and random selection are discretized and improved to adapt to the operation-based encoding. To compare the performance, different scale cases are generated in both the overall solution and the two-stage solution with the proposed algorithm, the hybrid particle swarm optimization algorithm and the hybrid genetic algorithm. The relative deviation is taken as the performance index. The compared results show that a better solution can be obtained with the proposed algorithm in both the overall solution and the two-stage solution, verifying the superior performance of the proposed algorithm.

Hybrid Harmony Search-Artificial Intelligence Models in Credit Scoring.

Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.

Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm

Abstract Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.

A novel harmony search algorithm and its application to data clustering

This paper presents a variant of harmony search algorithm (HS), called best–worst-mean harmony search (BWM_HS). The main difference between the proposed algorithm and the canonical HS is that it employs a modified memory consideration procedure to utilize more efficiently the accumulated knowledge and experience in harmony memory (HM). To this aim, the random harmony selection scheme of this procedure is replaced with three novel pitch selection and production rules. These rules use the information of the current best and worst harmonies as well as the mean of all harmonies to guide the search process. To further utilize the valuable information of HM, two new harmonies are generated at each iteration where the better one will compete with the current worst harmony. The mean of all harmonies is always employed to produce a new harmony. On the other hand, each pitch of the second one is obtained by the rules that consider the information of the best and worst harmonies. These rules can present either explorative or exploitative search behaviors at different stages of search. Thus, a probabilistic self-adaptive selection scheme decides to choose between them to properly balance the exploration and exploitation abilities. The general performance of BWM_HS for solving optimization problems is evaluated against CEC 2017 benchmark functions and its results are compared with HS and eight state-of-the-art variants of HS. The comparison indicates that the performance of BWM_HS is better than or equal to the compared algorithms with respect to the accuracy, robustness, and convergence speed criteria. Moreover, the performance of BWM_HS in solving clustering problems is investigated by applying it for clustering several well-known benchmark datasets. The experimental results show that, in general, the BWM_HS outperforms other well-known algorithms in the literature and in particular, it significantly improves the statistical results for one dataset.

Design of Mutated Harmony Search Algorithm for Data Dissemination in Wireless Sensor Network

In the recent years, the researcher has to face new challenges due to the complexity of technological an advances in Wireless Sensor Network (WSN). Sensor based networks are a special category of a distributed network that is used to provide communication among the sensor nodes. The wireless sensor network consists of various classes of sensors like thermal, infrared, optical, and seismic for the measurement of temperature, heat, radiation, humidity which requires constant monitoring and detection in specific events. A sensor’s with limited functionality both in computations, battery voltage keeps periodic sensing physical or environmental of ecological factors. Sporadic events such as detecting border intrusion, flood detection and habitat exploration of animals. The design of a WSN depends drastically on continuity and coverage of the network. Connected with that energy constrained is considered as one of the important issues to balance the network load and to extend the network life. An optimal energy efficient cluster based routing algorithm is required for effective data diffusion. Conventional protocol like LEACH, HEED, PEGASIS protocol etc., fails to balance the network load and the coverage area when the sensor nodes are deployed in large scale. In harmony search algorithm (HSA) absence of gradient search leads the parameter search is in the local region where the required optimal solution remains outside the local region. HSA is heuristic algorithm uses random search with constant harmony memory consideration rate. This paper focuses on designing a Meta-heuristic optimized routing protocol for a distributed network using mutated harmony search algorithm (MHSA) to improve the energy efficiency by simultaneously analyzing the cluster patching. Cluster patching is examined for improving network coverage and connectivity thereby to optimize the energy distribution in WSN. MHSA is a refinement of heuristic algorithm in the global search by adjusting the harmony memory consideration rate HMCR. To improve the performance and efficiency exact balancing of diversification and intensification is done by varying the Pitch adjusting rate PAR and bandwidth BW. Parametric results are compared with the standard heuristics algorithm HSA, GA, and PSO. The computational time and the experimental results show the proposed MHSA gives 85% of connectivity improved that ensures the success of cluster formation for an increased number of nodes to increase increases the network lifetime when compared with existing algorithms.

MH-CACA: multi-objective harmony search-based coverage aware clustering algorithm in WSNs

ABSTRACT In this paper, multi-objective tournament harmony search-based coverage aware load balanced clustering algorithm (MH-CACA) is presented. The coverage aware memory consideration, random selection and tournament selection-based pitch adjustment to remove clumps and filling voids is imposed while harmony improvisation by altering the status of the on-duty sensors to off-duty and off-duty sensors to on-duty respectively. To show the effectiveness a large sample network is considered and the results are demonstrated for different parameters including coverage rate, dead gateways, dead sensors, energy consumption and network lifetime in comparison to the other existing techniques and found significantly improved in the proposed approach.

A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

Exact models are a necessary prerequisite for optimal hardware configuration and the design of high-performance controllers. The photovoltaic system is considered a dynamic nonlinear multimodal system, where an optimization method must be used to resolve non-linearity and to identify the parameters describing the models of such systems. This has incited several researchers to work on and to develop several optimization methods. Recently, a number of methods have been proposed, including deterministic approaches, as well as probabilistic and stochastic numerical approaches, that aimed at obtaining a more accurate model for the PV cell and module array. This paper demonstrates the application of a performance optimization method based on discrete symbiotic organism search (DSOS), that mimics the behaviors of an organism in an ecosystem to survive. The high performance of such a method is attributable to the simplicity of the algorithm used; this algorithm is different from other heuristic algorithms, in that the GA needs two tuning parameters, i.e., the cross over and mutation rate, while the harmony search needs three rules to adjust and improvise new harmony, being memory consideration, pitch adjustment, and random choosing. Meanwhile, in the ABC algorithm, three phases are introduced to find the best food source, that is, the employed bee, the onlooker bee, and the scout bee phases, while the DSOS algorithm did not need any tuning parameters, wherein the proposed algorithm was used in both a single diode and double diode model across three test cases in the study. Compared to other previously published works, the level of performance of the algorithm is high in both implementation and accuracy; the DSOS algorithm is more capable of reaching the best set of solutions. The Mann-Whitney-Wilcoxon test to evaluate the discrete solutions of the algorithm for multiple runs with a 5% degree of confidence was evaluated and performed with a good level of accuracy.

A hybrid binary harmony search algorithm for solving the winner determination problem

The winner determination problem (WDP) in combinatorial auctions is to determine an allocation of items to bidders such that each item is allocated to at most one bidder, and the auctioneer's revenue is maximised. This paper proposes a hybrid binary harmony search algorithm for the WDP. Firstly, to enhance the global search ability of the proposed algorithm, a modified harmony improvisation mechanism is developed with a modified memory consideration rule and an adaptive pitch adjustment scheme. Next, a repair operator is employed to guarantee the feasibility of the new candidate harmonies. Finally, a tabu search procedure is presented to improve the local search ability. These strategies make a good balance between intensification and diversification. In the experiments, the performance of the proposed algorithm is validated on five groups of 500 instances. Experimental results and comparisons show that the proposed algorithm is very efficient, and the tabu search procedure significantly improves the performance of the proposed algorithm.

An improved harmony search algorithm for optimized link state routing protocol in vehicular ad hoc network

Vehicle Ad-hoc Network (VANET) is a direct application of Mobile Ad-hoc Network (MANET). Nodes in VANET are vehicles that communicate using vehicle to vehicle (V2V) or vehicle to infrastructure (V2I). These types of communications have led to the emergence of various applications that provide safer driving. Due to the high changing of topology and frequent fragmentation of VANET, routing pack-ets in this type of network is a hard task. In this work, the authors deal with the well-known MANET proactive Optimized Link State Rout-ing protocol (OLSR). The deployment of OLSR in VANET gives the moderate performance; this is due to its necessity of constant ex-changing of control packets. The performance of OLSR is highly dependent on its parameters, thus finding optimal parameters configura-tions that best fit VANETs environment and improves the network is essential before its deployment. Therefore, this research proposes a modified Harmony Search optimization (HSO) by incorporating selection methods in its memory consideration; roulette wheel selection to obtain fine-tuned OLSR for high density and velocity scenario. The experimental analysis showed that the OLSR with the proposed ap-proach acquired promising results regarding packet delivery ratio, end-to-end delay and overhead when compared with previous approaches.

Binary Moth Search Algorithm for Discounted {0-1} Knapsack Problem

The discounted {0-1} knapsack problem (DKP) extends the classical 0-1 knapsack problem (0-1 KP) in which a set of item groups is included and each group consists of three items, whereas at most one of the three items can be packed into the knapsack. Therefore, the DKP is more complicated and computationally difficult than 0-1 KP. The DKP has been found many applications in real economic problems and other areas. In this paper, the influence of Levy flights operator and fly straightly operator in the moth search (MS) algorithm is verified. Nine types of new mutation operator based on the global harmony search are specially devised to replace Levy flights operator. Then, nine novel MS-based algorithms for DKP are proposed (denoted by MS1–MS9). Extensive experiments on three sets of 30 DKP instances demonstrate the remarkable performance of the proposed nine new MS-based approaches. In particular, it discovers that MS1–MS3 show better comprehensive performance among 10 algorithms. A variety of analyses indicate the important contribution of the individual of memory consideration in MS1–MS9.

Modified Parameters of Harmony Search Algorithm for Better Searching

The scheduling and rostering problems are deliberated as integrated due to they depend on each other whereby the input of rostering problems is a scheduling problems. In this research, the integrated scheduling and rostering bus driver problems are defined as maximising the balance of the assignment of tasks in term of distribution of shifts and routes. It is essential to achieve is fairer among driver because this can bring to increase in driver levels of satisfaction. The latest approaches still unable to address the fairness problem that has emerged, thus this research proposes a strategy to adopt an amendment of a harmony search algorithm in order to address the fairness issue and thus the level of fairness will be escalate. The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration. These parameters influence the overall performance of the HS algorithm, and therefore it is crucial to fine-tune them. The contributions to this research are the HMCR parameter using step function while the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter. The model of constant step function is introduced in the alteration of HMCR parameter. The experimental results revealed that our proposed approach is superior than parameter adaptive harmony search algorithm. In conclusion, this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers, which contributed to the lowering of illness, incidents, absenteeism and accidents.

Harmony search based remodularization for object-oriented software systems

Software remodularization is always a key task in the field of software reengineering. In recent years, search-based optimization techniques have been considered as an effective method to handle software remodularization problems. Recently, Harmony Search (HS), a metaheuristic algorithm has gained wide attention and has been demonstrated to be effective and convenient to solve various science and engineering problems. The applicability and usefulness of HS algorithm has not been studied by any researcher till date to solve the software remodularization problem. This paper proposes a Harmony Search-Based Remodularization Algorithm (HSBRA) to solve the software remodularization problem for object-oriented software (OOS) systems. To do so, several key improvements have been put forward like an efficient encoding of harmony memory, initialization of harmony memory, an effective strategy for improvisation of a new harmony. In addition, a new fitness function that considers coupling, cohesion, package count index and package size index is developed. Four different variants of HSBRA (i.e., HSBRA1, HSBRA2, HSBRA3, and HSBRA4) based on linear and exponential changes in Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR) have been formulated. The proposed approach is tested over 8 problem instances and results are compared with both the population based (Genetic Algorithm - GA, Differential Evolution – DE, and Artificial Bee Colony - ABC) and single-solution based (Simulated Annealing - SA and Hill-Climbing - HC) algorithms. A Wilcoxon test is performed to assess the pair wise statistical performance of the algorithms. The results show that HSBRA outperforms SA, HC, and GA algorithms and performs better than ABC algorithms. Out of four variants of HSBRA, exponential change based variants of HSBRA perform better than linear change based variants.

Tournament-based harmony search algorithm for non-convex economic load dispatch problem

This paper proposes a tournament-based harmony search (THS) algorithm for economic load dispatch (ELD) problem. The THS is an efficient modified version of the harmony search (HS) algorithm where the random selection process in the memory consideration operator is replaced by the tournament selection process to activate the natural selection of the survival-of-the-fittest principle and thus improve the convergence properties of HS. The performance THS is evaluated with ELD problem using five different test systems: 3-units generator system; two versions of 13-units generator system; 40-units generator system; and large-scaled 80-units generator system. The effect of tournament size (t) on the performance of THS is studied. A comparative evaluation between THS and other existing methods reported in the literature are carried out. The simulation results show that the THS algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.