Original ABC Research Articles

Early Breathing in Very Preterm Infants during Deferred Cord Clamping (DCC) Is Related to Gestational Age and Is Not Independently Associated with Important Neonatal Outcomes: A 5-Year Cohort Study.

Deferred cord clamping (DCC) has been associated with reduced mortality in preterm infants, and a period of at least 30 s has been recommended before clamping. However, preterm infants assessed as being in need of resuscitation have often had earlier cord clamping. In this study, we aimed to compare neonatal outcomes for preterm infants undergoing DCC who established early breathing movements compared to those who were not breathing. After a 5 yr recruitment period, we recently completed the ABC study, in which preterm infants <31 weeks undergoing 50 s of DCC who were not breathing by 15 s of age were randomised into two groups: one received intermittent positive pressure ventilation (IPPV) and the other was a standard group, which received no breathing support. The outcomes in the two groups were similar, and for the present analysis, the groups were combined. Infants in the ABC study were compared with the cohort excluded from the original ABC study because they were breathing by 15 s (called the Breathing Before Clamping or BBC group). There were significant differences in demographics between the ABC and BBC groups. Spontaneous preterm labour was more common in the BBC group, and these infants were more likely to be delivered vaginally. Gestational age and birth weight were significantly higher in the BBC group (p < 0.01). Soon after birth, Apgar scores were significantly higher in the BBC group, with a lower base deficit on first obtained blood gas, and a smaller proportion were intubated in the delivery room. Fewer BBC infants were hypothermic (<36.5 °C) on admission. Multivariate regression analysis indicated whether infants were breathing or not at 15 s of age was linked predominantly to gestation. Important neonatal outcomes and a composite of these outcomes (mortality, severe intraventricular haemorrhage, bronchopulmonary dysplasia) were not significantly different between the ABC and BBC groups (odds ratio for the composite outcome was 1.77 CI 0.84-3.76 corrected for gestation). For very preterm infants undergoing DCC, important neonatal outcomes were related to gestational age and not independently associated with early breathing. There was a small group (7% of total) who were deemed compromised at birth and did not undergo DCC. These infants had significantly worse neonatal outcomes.

Using the Modified Artificial Bee Colony Algorithm to Find the Non-Archimedean Epsilon for Evaluating the Efficiency in DEA

The artificial bee colony algorithm is one of the population-based optimization methods inspired by the evolutionary principles of the social behavior of bees. On the other hand, one of the sub-fields of operations research science is data envelopment analysis. There are some difficulties in DEA models for selecting the appropriate numerical value for an infinitesimal non-Archimedean epsilon. So far, various methods have been proposed to solve this problem and choose the suitable non-Archimedean epsilon. In order to solve the problem, the artificial bee colony algorithm (ABC), and modification of the original ABC algorithm (MABC) are adopted and proposed in this paper. The impacts of our proposed algorithms on the suitable non-Archimedean epsilon by solving only one linear programming (LP), instead of n LP are investigated. Finally, the performance of the proposed algorithms is evaluated by comparing the solutions obtained from GAMS software based on the presented examples.

Optimal Trading in Local Electricity Market Based on an Enhanced Evolutionary Algorithm with Distribution Estimation

The evolution from passive to active distribution network, the ability to actively control distributed energy resources and to perform bilateral energy exchange has significantly complicated the market transactions in recent years due to activities among consumers and prosumers. Local electricity market (LEM) is challenged to optimize welfare as well as reduction of emissions by involving participants in energy transactions. In this paper, the trading strategy is modeled as a single-objective optimization problem to determine each agent's optimal energy trading. Modern heuristic optimization techniques have proven their efficiency and robustness in large optimization problems and thus, an enhanced artificial bee colony (EABC) incorporating a distribution estimation algorithm is proposed, which guides the search for optimum by building and sampling explicit probabilistic models of promising candidate solutions. With the help of a distribution estimation algorithm, the EABC eliminates many tuning parameters in the original ABC and increases its exploitation to obtain more robust and competitive performance. A comparative study is conducted with the basic ABC, differential evolution, and particle swarm optimization, and the EABC demonstrates its efficiency on the case study where nine agents are involved in trading energy in the day-ahead LEM.

Indoor Comfort and Energy Consumption Optimization Using an Inertia Weight Artificial Bee Colony Algorithm

A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in maximizing the user’s comfort level. Nonetheless, the energy consumption of these appliances needs to be taken into consideration to minimize the operational cost and at the same time provide an environmentally friendly system. Comfort level maximization and energy consumption minimization are optimization problems. This issue is becoming more important due to the lifestyle changes caused by the COVID-19 pandemic that resulted in more time spent at home and indoors. Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. The multi-objective problem is tackled as a weighted single objective optimization problem. The proposed solution is tested using a dataset of 48 environmental conditions. The results of the simulation show that the IW-ABC performs better than the original ABC and other benchmark algorithms and the IW-ABC with linear increasing inertia weight has the most improved convergence behavior.

Logic-Depth-Aware Technology Mapping Method for RSFQ Logic Circuits With Special RSFQ Gates

This article proposes a method for generating RSFQ logic circuits utilizing special RSFQ gates in logic synthesis. It is suited for the standard technology mapping flow used in logic synthesis tools and enables generating RSFQ circuits utilizing confluence buffers (CBs) and resettable DFFs (RDFFs), to reduce the number of clocked gates by reduction of logic depth. A library of supergates, each consisting of several gates, is used to compose a resultant circuit in the technology mapping. In this article, a library containing supergates including those special gates is generated and is utilized to design circuits with the gates. First, a generation of a supergate library is proposed to introduce CBs and RDFFs, which realize logic OR without clock pulses and a fused function of logic AND and NOT, respectively. The generated library contains supergates including the special gates and they are used to compose the resultant circuits with the special gates. A mapping approach is also proposed. It distinguishes non-clocked gates such as CBs and clocked gates. It takes the number of inserted DFFs for path balancing into account to reduce the number of clocked gates. The method was implemented into the state-of-the-art academic EDA tool, i.e., ABC, and was evaluated. Evaluation results show that the number of clocked gates is reduced by about 30% from the original ABC with path-balancing DFFs.

UTF: Upgrade transfer function for binary meta-heuristic algorithms

In the real world, many optimization problems are discrete and very complex to solve. Some of them are in the class of NP-hard problems and their search spaces grow exponentially with the problem size. As a result, an exhaustive search will be impractical using exact algorithms. In the last decades, meta-heuristic algorithms as approximate algorithms have shown superior performance in solving these problems. The majority of these algorithms have been designed for continuous search spaces and are not able to solve binary optimization problems. Therefore, a transfer function is applied to convert the continuous search space to the binary one. The performance of such binary algorithms depends on their ability of exploration, exploitation and transfer function. Several transfer functions have been introduced so far but they have shown poor exploration and exploitation in solving some problems. In this study, a novel adaptive transfer function, based on two linear functions, is proposed to overcome the shortcomings of existing transfer functions. The proposed method called upgrade transfer function (UTF) adapts itself during running the algorithm to switch from exploration to exploitation. This capability also covers disadvantages of metaheuristic algorithms in terms of poor exploration and exploitation. The performance of UTF has been evaluated by three discrete optimization problems: function optimization, feature selection and the 0–1 multi-knapsack problem (MKP). The results of binary particle swarm optimization (BPSO), binary artificial bee colony (BABC) and several improved BPSO and BABC have been compared with those of UTF–BPSO and UTF–BABC using function optimization problems. Also, the efficiency of UTF–BPSO and UTF–BABC and some binary meta-heuristic algorithms such as binary salp swarm algorithm (BSSA) and binary gray wolf optimization (bGWO), binary dragon algorithm (BDA), binary multi-neighborhood artificial bee colony (BMNABC), binary hybrid topology particle swarm optimization quadratic interpolation (BHTPSO-QI), binary ant lion optimizer (bALO) and binary gravitational search algorithm (BGSA) have been evaluated by feature selection problems. Moreover, UTF and seventeen transfer functions have been applied in original PSO, ABC, SSA and GWO algorithms to solve low and high dimensions 0–1 MKP benchmark instances. The results showed that the new transfer function significantly enhances the performance of algorithms to achieve the best solution in the binary search space.

A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called β-hill climbing (βHC) and denoted by ABC–βHC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC–βHC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon’s rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC–βHC is statistically significant.

Parameters estimation using mABC algorithm applied to distributed tracking control of unknown nonlinear fractional-order multi-agent systems

This paper addresses the parameters estimation and distributed tracking control of unknown nonlinear fractional-order multi-agent systems (FOMASs) with external disturbances from the viewpoint of optimization. Firstly, a modified artificial bee colony (mABC) algorithm is put forward to enhance the exploration and exploitation abilities of the original ABC algorithm. Secondly, a parameters estimation approach based on the above mABC algorithm is proposed to estimate the unknown fractional orders and system parameters, where the parameters estimation is converted into a multi-dimensional functional optimization problem. Thirdly, the obtained parameters estimation values are further utilized to construct a distributed control algorithm for solving the tracking control problem. Finally, the experimental results illustrate that the proposed approaches can accurately estimate and robustly control such unknown nonlinear FOMASs.

Kernel Estimation of Volterra Using an Adaptive Artificial Bee Colony Optimization and Its Application to Speech Signal Multi-Step Prediction

In order to solve parameters selection problem when applying recursive least square (RLS), least mean square (LMS) or normalized LMS (NLMS) algorithms to estimate kernels of second-order Volterra filter (SOVF), a novel adaptive gbest-guide artificial bee colony (AGABC) optimization algorithm is used to derive kernels of Volterra, that is a type of the AGABC-SOVF prediction model with an explicit configuration for speech signal is proposed. The AGABC algorithm modifies the solution search equation of ABC algorithm and combines the best solution with neighborhood information at present iteration, which not only ensures the exploration of the global optimization algorithm but also improves the exploitation. The AGABC-SOVF model is performed to predict speech signal series of the given English phonemes, sentences, and chaotic time series. Simulation results based on benchmark function show that AGABC algorithm performs faster convergence in achieving higher quality solutions than original ABC and other improved ABC algorithms. Prediction results of applying the AGABC-SOVF model to multi-step predictions for Lorenz time series reveal its stability and convergence properties. For the measured multi-frame speech signals, prediction accuracy and length of multi-step prediction using the AGABC-SOVF model are better than that of the ABC-SOVF model. The AGABC-SOVF model can better predict chaotic time series and the real measured speech signal series.

Automatic clustering using an improved artificial bee colony optimization for customer segmentation

In cluster analysis, determining number of clusters is an important issue because information about the most appropriate number of clusters do not exist in the real-world problems. Automatic clustering is a clustering approach which is able to automatically find the most suitable number of clusters as well as divide the instances into the corresponding clusters. This study proposes a novel automatic clustering algorithm using a hybrid of improved artificial bee colony optimization algorithm and K-means algorithm (iABC). The proposed iABC algorithm improves the onlooker bee exploration scheme by directing their movements to a better location. Instead of using a random neighborhood location, the improved onlooker bee considers the data centroid to find a better initial centroid for the K-means algorithm. To increase efficiency of the improvement, the updating process is only applied on the worst cluster centroid. The proposed iABC algorithm is verified using some benchmark datasets. The computational result indicates that the proposed iABC algorithm outperforms the original ABC algorithm for automatic clustering problem. Furthermore, the proposed iABC algorithm is utilized to solve the customer segmentation problem. The result reveals that the iABC algorithm has better and more stable result than original ABC algorithm.

A novel artificial bee colony algorithm with local and global information interaction

The artificial bee colony algorithm (ABC) is a new stochastic and population-based optimization method, which has been attracting a great deal of attention, due to its simple structure, easy implementation and outstanding performance. However, it also suffers from slow convergence like other evolutionary algorithms. In order to address this concerning issue, in this paper, we propose a novel artificial bee colony algorithm with local and global information interaction, called ABCLGII. In employed bee phase, each employed bee is designed to learn from the best individual among its neighbors or in a local visible scope. By this way, the search of employed bees is no longer independent and blind, but is cooperative and directional, such that a local information interaction mechanism is conducted between employed bees. In onlooker bee phase, only a part of superior food sources have chance to attract onlooker bees to exploit in their vicinity. Moreover, two novel search equations are proposed for onlooker bees to generate candidate food sources. Specifically, one exploits the useful information of some good solutions, while the other combines the valuable information of the current best solution and some good solutions simultaneously. An adaptive selection mechanism is accordingly designed for onlooker bees to choose a proper search equation for producing candidate food sources. In this way, a global information interaction mechanism is employed for onlooker bees. In order to evaluate the performance of ABCLGII, we compare ABCLGII with the original ABC and other outstanding ABC variants on 52 frequently used test functions. The experimental results show that ABCLGII is better than or at least competitive to the state-of-the-art ABC variants in terms of solution quality, robustness and convergence speed.

An improved multi-cores parallel artificial Bee colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee the population’s diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash–Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters’ optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.

Multi-objective service composition model based on cost-effective optimization

The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users’ requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective Artificial Bee Colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.

Many-objective artificial bee colony algorithm for large-scale software module clustering problem

The meta-heuristic search algorithms have been widely applied to solve the various science and engineering optimization problems. However, the performance of these algorithms is highly sensitive toward the number of objective functions and number of decision variables. Recently, it has been explored by the researchers that the performance of such algorithm degrades when the number of objective functions and decision variables increases by some limit. Hence, these algorithms can be hardly acceptable to the real-world optimization problems such as software module clustering problem (SMCP), which contains a large number of objective functions and decision variables. Previous researchers have proposed several approaches to address the many-objective optimization problems by revising existing meta-heuristic algorithms. Recently, an artificial bee colony algorithm (ABC), a meta-heuristic algorithm, effectively used to address the several multi-objective optimization problems. Even though in most of the cases ABC algorithm performs better compared to other meta-heuristic algorithms, it faces the same problems as other meta-heuristic algorithms for a large number of objective functions and decision variables. This paper proposes a many-objective artificial bee colony (MaABC) algorithm to solve many-objective SMCPs. In this contribution, we revised the original ABC by using, quality indicator, $$L_{p}$$ -norm-based (p < 1) distances, and two external archives concepts. To validate the proposed approach, an extensive comparative study is performed with the existing many-objective optimization algorithms (i.e., Two-Arch2, NSGA-III, MOEA/D, and IBEA) over seven SMCPs. The statistical analysis of the results show that the proposed MaABC outperforms existing many-objective approaches in terms of modularization quality (MQ), cohesion, coupling, and inverted generational distance (IGD).

A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion

An Online Vehicle Routing Problem is a formation of Capacitated Vehicle Routing Problem with re-routing strategy to resolve the problem of inefficient vehicle routing caused by traffic congestion. A flexible delivery rerouting strategy is proposed, which aims at reducing the risk of late delivery. The method of terminating an exploration in a solution by the original ABC algorithm, when the solution is trapped in local optima, is to abandon the solution after specific tolerance limits are set. The phenomenon of local optimal traps will be repeated rapidly after a lengthy recursive process and will eventually result in a low quality solution, with a more complex combinatorial problem when the capability of the exploration is restricted by an inflexible termination criterion. Therefore, this paper proposes a novel scheme using a Multiple Colonies Artificial Bee Colony algorithm. The designs of the outstanding bee selection for colony communication show it to be superior in exploitation. The performance of the proposed algorithm is examined through by Capacitated Vehicle Routing instances and a case study, and the results indicate the potential of using real time information for data-driven vehicle scheduling.

An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.

ABC Shadow algorithm: a tool for statistical analysis of spatial patterns

This paper presents an original ABC algorithm, ABC Shadow, that can be applied to sample posterior densities that are continuously differentiable. The proposed algorithm solves the main condition to be fulfilled by any ABC algorithm, in order to be useful in practice. This condition requires enough samples in the parameter space region, induced by the observed statistics. The algorithm is tuned on the posterior of a Gaussian model which is entirely known, and then, it is applied for the statistical analysis of several spatial patterns. These patterns are issued or assumed to be outcomes of point processes. The considered models are: Strauss, Candy and area-interaction.

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.

Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model

Large-scale radio frequency identification (RFID) network planning (RNP) problem has been proven to be a NP-hard issue, which can be formulated as a high-dimensional nonlinear optimization problem with a mixture of discrete and continuous variables and uncertain parameters. First, a two-level optimization model for RFID network planning based on distributed decision making (DDM) is presented in this paper. In this model, the mixed discrete and continuous planning variables, namely the number, location, and radiate power of RFID readers are optimized. In each level of the optimization model, the different objectives to determine optimal values for these planning variables are as follows: (i) minimization of total installation cost of RFID network in the top-level; (ii) maximization of tag coverage and network reliability, and minimization of reader interference in the lower-level. In order to solve the proposed model effectively, this work proposes an efficient approach for RNP problem, namely the hybrid artificial bee colony optimizer (HABC), which employs the natural life-cycle mechanism to cast the original ABC framework to a cooperative and population varying fashion. In the proposed HABC, individuals can dynamically shift their survival states and population size varies dynamically according to the local fitness landscape during the executions of algorithm. These new characteristics of HABC help to avoid redundant search and maintain diversity of population in complex environments. Experiments are conducted on a set of CEC2005 and discrete benchmarks for evaluating the proposed algorithm. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

Applying portfolio theory-based modified ABC to electricity generation mix

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy), installed capacity (power) and actual instantaneous power delivery is proposed. Therefore, in this model, the uncertainties of wind power and ramp-up/down constrains of traditional power plants are correctly considered in the investment cost. The numerical results show the great potential of proposed model with lowest risk on generation cost. Also, they are show that MABC approach is successful in portfolio optimization.