Modified Artificial Bee Colony Research Articles

An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>An example of a combinatorial problem is the vehicle routing problem with time windows (VRPTW), which focuses on choosing routes for a limited number of vehicles to serve a group of customers in a restricted period. Meta-heuristics algorithms are successful techniques for VRPTW, and in this study, existing modified artificial bee colony (MABC) algorithm is revised to provide an improved solution. One of the drawbacks of the MABC algorithm is its inability to execute wide exploration. A new solution that is produced randomly and being swapped with best solution when the previous solution can no longer be improved is prone to be trapped in local optima. Hence, this study proposes a perturbed MABC known as pertubated (P-MABC) that addresses the problem of local optima. P-MABC deploys five types of perturbation operators where it improvises abandoned solutions by changing customers in the solution. Experimental results show that the proposed P-MABC algorithm requires fewer number of vehicles and least amount of travelled distance compared with MABC. The P-MABC algorithm can be used to improve the search process of other population algorithms and can be applied in solving VRPTW in domain applications such as food distribution.</span></p></div></div></div>

The ride-hailing sharing problem with parcel transportation

This paper proposes a ride-hailing sharing problem with parcel transportation. In the study, both passengers and parcels can be transported by ordering the requests through a ride-hailing platform, in which the passengers can only be delivered by ride-hailing vehicles (RHVs) while parcels can be delivered by both RHVs and electric motorcycles (EMs). Compared with RHVs, EMs have a lower speed, a smaller capacity for parcels, and a lower driving cost per kilometer. Parcels are divided into eight classes according to the size of parcels (i.e., extra small, small, middle, and large) and the urgency of parcels (i.e., urgent and non-urgent). Passengers and passengers, passengers and parcels, as well as multiple parcels, are allowed to share the same vehicles in this problem. A two-level lexicographic multi-objective function is proposed from the perspective of the ride-hailing platform, where the first-level objective is to maximize the profit of the platform and the second-level objective is to minimize the total driving cost of RHVs and EMs. The time-varying property of passenger requests is considered and the proposed problem is divided into a set of continuous and small-scale ride-hailing subproblems. Each subproblem is formulated to match both passenger and parcel requests to RHVs and EMs, and to determine vehicle routes. Time window constraints, RHV capacity constraints for passengers, RHV trunk capacity constraints for parcels and luggage, and EM delivery box capacity constraints are considered. The modified artificial bee colony (MABC) algorithm is proposed based on the framework developed by Zhan et al. (2022) to solve the ride-hailing sharing problem with parcel transportation. The results of numerical experiments show that allowing RHVs to transport parcels can significantly increase the profit of the platform, the number of matched requests, and the average profit of RHV drivers. If RHVs are allowed to pick up parcels, the government should encourage the replacement of all EMs with RHVs under the more-passengers-fewer-parcels scenario. However, the government should not encourage the replacement of all EMs with RHVs and allow some EMs in the ride-hailing market under the fewer-passengers-more-parcels scenario.

Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.

Pharmaceutical R &D portfolio optimization with minimum borrowed capital based on fuzzy set theory

Due to long lead times, uncertain outcomes and lack of enough historical data, pharmaceutical research and development (R &D) portfolio selection is a often very complex decision issue. The aim of this paper is to investigate pharmaceutical R &D portfolio selection with unavailable and unreliable project information, where the borrowed capital is allowed. Based on fuzzy set theory, we propose a pharmaceutical R &D portfolio optimization model with minimum borrowed capital by taking into account corporate strategy in developing new products, scarcity of resources, lack of investment budget and cardinality constraint. In the proposed model, the pharmaceutical R &D company is assumed to achieve the objectives of maximizing terminal wealth and minimizing the cumulative borrowed capital over the whole investment horizon. Then, we transform the proposed bi-objective model into the corresponding single-objective model by using the weighted sum approach and employ the modified artificial bee colony (MABC) algorithm to solve the transformed model. Finally, we provide a numerical example to illustrate the application of our model.

A modified artificial bee colony based fuzzy motion tracking scheme for mobile robot

In this study a new modified artificial bee colony algorithm for the optimization of the fuzzy control scheme for motion tracking of mobile robot is developed. The modification is based on using some features from the particle swarm optimization algorithm to improve solution quality. The modified artificialbee colony (MABC) balance the exploration and exploitation of the original one. This balancing results in going through the global search space and increases the convergence speed and solution accuracy. MABC is then used for the design of an efficient fuzzy system that perform motion tracking for mobile robot more accurate through minimizing a suitable selected objective function. Results illustrate the high quality of the proposed method.

MABC‐EPF: Video in‐painting technique with enhanced priority function and optimal patch search algorithm

AbstractIn this article, video in‐painting technique with enhanced priority function and optimal patch searching algorithm is proposed. Initially, the source frames are extracted from video based on edge information, these frames are partitioned into two regions (1) target region (region to be in‐painted) and (2) source region that is region to be considered as appropriate patch selection for in‐painting the target region. A new priority function using confidence value, local structure multiplier, and motion vector uniformity to determine the in‐paint location. The suitable patch is identified in source region, using correlation measure, like sum of squared difference method. The searching of the suitable patch in source region is optimized through modified artificial bee colony (MABC) algorithm. To capture more edge information, irregular patch filling is carried out to in‐painting process. The in‐painting process is more improved by optimal patch scaling for better vision. Finally, the in‐painted source frames are combined to obtain the corrected video. The proposed method works better than the other art state applications. The proposed peak signal to noise ratio (PSNR) attains better 8.06%, 7.90%, 32.15%, and 13.06% compared with other previous methods.

Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem

Ride-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel time compared with the case without ride-hailing sharing.

A Novel Modified Artificial Bee Colony for DOA Estimation

Aims: We consider the Direction of Arrival (DOA) estimation for code division multiple access (CDMA) signals. Background: Solving this problem requires non-linear optimization and thus the speed of convergence becomes crucial. Objective: A novel Modified Artificial Bee Colony (MABC) has been proposed. We use secondorder Taylor series expansion of the cost function to ameliorate the searchability of artificial bee colony (ABC) for finding the globally optimal solution. Methods: The main idea is to harness the exploration and exploitation features. The optimum point of second-order Taylor expansion of cost function is used as a velocity factor of the ABC algorithm. Results: The proposed technique is used for solving the DOA estimation problem of a CDMA system. Simulation results confirm the performance improvement of our proposed algorithm. Conclusion: The cost function of the DOA estimation usually leads to a non-linear optimization problem. Using evolutionary algorithms can improve convergence rate of such problems.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

Peripheral pulse multi-Gaussian decomposition using a modified artificial bee colony algorithm

Time-domain analysis is one of widely used methods to acquire physiological information from peripheral pulse waves. It is crucial to precisely extract features from pulse waves so that classifications can be performed to distinguish differences with high accuracy. Curve fitting has been proved to be more effective than points-based detection for modeling pulse signal trends, and the Gaussian function is a popular model because its bell shape is like an individual pulse. The pulse decomposition problem is exactly an optimization problem, which can be solved by swarm algorithms. In this study, we use Gaussian-mixture functions to fit practical pulse waves by an artificial bee colony (ABC) algorithm. A modified ABC (MABC) algorithm is compared with classical ABC for performance evaluation. MABC is adapted from the classical ABC by modifications in searching strategies of employed bees, onlooker bees, and scout bees. It is concluded that MABC converges quickly under limited iteration times and is effective up to a dimension of 30 for a single complete cycle. We collected pulse waves from a group of 46 healthy volunteers and a group of 69 type-2 diabetic candidates in a clinic. A support vector machine (SVM) with radial basis functions (RBFs) was used for classification after pulse decomposition; the training and test data were obtained randomly at a 7:3 ratio. The largest accuracy averaged 89.1% by considering combinations of the first, third, and fourth center location values, and the first amplitude value of Gaussian functions.

Artificial-Noise-Aided Energy-Efficient Secure Multibeam Wireless Communication Schemes Based on Frequency Diverse Array

In this paper, we research synthesis scheme for secure wireless communication in multibeam directional modulation (MBDM) system, which consists of multiple legitimate users (LUs) receiving their own individual confidential messages, respectively, and multiple eavesdroppers (Eves) intercepting confidential messages. We propose a new type of array antennas, termed frequency diverse arrays (FDA), to enhance security of confidential messages. Leveraging FDA technology and artificial noise (AN) technology, we aim to address the PHY security problem for MBDM by jointly optimizing the frequency offsets, the precoding matrix and the AN projection matrix. In the first stage, with known locations of Eves, precoding matrix is designed to minimize Eve’s receiving power of confidential message (Min-ERP), while satisfying power requirement of LUs. And then artificial-noise projection matrix (ANPM) is calculated to enhance AN impact on Eves without influencing LUs. Furthermore, we research a more practical scenario, where locations of Eves are unknown. Unlike the scenario of the known locations of Eves, precoding matrix is designed to maximize AN transmit power (Max-ATP), while satisfying each LU’s requirement received power of confidential message. In the second stage, we analyze and further optimize secrecy capacity. The problem is solved by optimizing frequency offsets through modified artificial bee colony (M-ABC) algorithm. Numerical results show that the proposed scheme can achieve a secure transmission in MBDM system.

Acceleration harmonics estimation and elimination with MABC–RLS algorithm: Simulation and experimental analyses on shaking table

In this paper, to estimate and eliminate acceleration harmonics in shaking table system, a novel approach combining modified artificial bee colony (MABC) algorithm and recursive least square (RLS) algorithm is proposed. MABC algorithm is employed in amplitude and phase estimation of acceleration harmonics while RLS algorithm is used to eliminate harmonics from the acceleration signal. In order to analyse the performance of the proposed algorithm, simulation and experimental studies are realized and the results are compared with the well-known algorithm reported in literature. In addition, harmonic frequencies and numbers are increased and robustness of proposed approach is investigated.

A modified ABC algorithm for solving optimal power flow problem

This paper presents a modified artificial bee colony (MABC) algorithm for solving the optimal power flow (OPF) problem in power system. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The new algorithm is implemented to the OPF problem so as to minimize the total generation cost when considering the equality and inequality constraints. In order to validate of the proposed algorithm, it is applied to the standard IEEE 30-bus test system. The results show that the proposed technique provides better solutions than other heuristic techniques reported in literature.

A modified algorithm for computation issues in UAV-enabled wireless communications

Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm.

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

Modified Artificial Bee Colony Algorithm with Multilevel Threshold Segmentation and Boundaries Evaluation for Shadow Detection

The appearance of shadows typically causes severe problems in pc vision. There are various methods have already put forward but scope in this field is open. In this article Shadow Detection and Removal Using Modified artificial bee colony (MABC) Algorithm with Multilevel Threshold segmentation is proposed. The proposed method uses three threshold and corresponding boundaries, associated curvature, edge response, gradient, and MABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three threshold and corresponding boundaries evaluated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the boundaries. Finally, MABC has been applied for detecting the shadow. The results show improvement in comparison with other existing methods.

An image classification framework exploring the capabilities of extreme learning machines and artificial bee colony

A hybridized image classification strategy is proposed based on discrete wavelet transform, artificial bee colony (ABC) and extreme learning machine (ELM). The proposed methodology works in three phases: (a) in preprocessing phase, images are decomposed and features are extracted from images using bi-orthogonal wavelet functions; (b) secondly, modified ABC (MABC) optimization algorithm is proposed to determine the optimal parameters such as hidden layer weights and biases to be used by ELM for classification; (c) the ELM in the third phase has been trained and tested with three brain image datasets for different diseases along with normal brain images. The performance recognition of the proposed MABC-ELM in terms of accuracy, rate of per-image classification and speedup has been made with variants of ELM such as ELM, ABC-ELM and MABC-ELM and also with MLPNN, naive Bayesian, linear regression classifiers. Finally, the percentage of accuracy observed by the proposed MABC-ELM, for acute stroke-speech arrest, glioma and multiple sclerosis datasets, is 90%, 90% and 100% with eight hidden nodes in the ELM architecture, and it can be concluded that MABC-ELM gives better generalization performance, more compact network architecture and the hybridization of ELM with modified ABC is worth investigated.

Parameter Space Exploration for Analog Circuit Design Using Enhanced Bee Colony Algorithm

In this work, novel swarm optimization algorithm based on the Artificial Bee Colony (ABC) algorithm called Enhanced Artificial Bee Colony (EABC) algorithm is proposed for the design and optimization of the analog CMOS circuits. The new search strategies adopted improve overall performance of the proposed algorithm. The performance of EABC algorithm is compared with other competitive algorithms such as ABC, GABC (G-best Artificial Bee Colony Algorithm) and MABC (Modified Artificial Bee Colony Algorithm) by designing three CMOS circuits; Two-stage operational amplifier, low-voltage bulk driven OTA and second generation low-voltage current conveyor in 0.13 [Formula: see text]m and 0.09[Formula: see text][Formula: see text]m CMOS technologies. The obtained results clearly indicate that the performance of EABC algorithm is better than other mentioned algorithms and it can be an effective approach for the automatic design of the analog CMOS circuits.