Artificial bee colony (ABC) and its most modifications use a probability method to select good food sources (called solutions) in the onlooker bee search phase. However, the probability selection ...does not work with increasing of iterations, because the fitness values cannot be used to distinguish two different solutions. In order to tackle this problem, this paper proposes a new ABC (called NSABC), in which a new selection method based on neighborhood radius is used. Unlike the probability selection in the original ABC, NSABC chooses the best solution in the neighborhood radius to generate offspring. Based on the neighborhood radius, two new solution search strategies are modified. The scout bee search phase is improved by using opposition-based learning and the neighborhood radius. To evaluate the search ability of NSABC, there are 22 benchmark problems used in the experiments. Performance comparison shows NSABC achieves better results than five other ABC algorithms.
•ABC is good at exploration but poor at exploitation.•Different characteristics of strategies can be appropriate during different stages.•A pool of distinct strategies coexists throughout the search ...process.•The proposed approach achieves a tradeoff between exploration and exploitation.
Artificial bee colony (ABC) is a recently proposed optimization technique which has shown to be competitive to other population-based stochastic algorithms. However, ABC is good at exploration but poor at exploitation because of its solution search strategy. Thus, to obtain an efficient performance, utilizing different characteristics of solution search strategies can be appropriate during different stages of the search process to achieve a tradeoff between exploration and exploitation. In this paper, we propose a novel multi-strategy ensemble ABC (MEABC) algorithm. In MEABC, a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring. Experiments are conducted on a set of commonly used numerical benchmark functions, including the CEC 2013 shifted and rotated problems. Results show that MEABC performs significantly better than, or at least comparable to, some well-established evolutionary algorithms.
Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in ...various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
Based on the analysis of multi-objective flexible job-shop scheduling problem (FJSP), a multi-objective low-carbon job-shop scheduling problem(MLFJSP) with variable processing speed constraint is ...proposed in this paper. The optimization objectives of MLFJSP include minimizing the makespan, total carbon emission and machine loading. Meanwhile, an improved artificial bee colony algorithm (IABC) is designed to solve the MLFJSP. The improvement of algorithm mainly includes: (1) an effective three-dimensions encoding/decoding mechanism and a mixed initialization strategy are designed to generate a better initial population; (2) special crossover operators and mutation operators were designed to increase the diversity of the population in the employed bee phase; (3)an efficient dynamic neighbor search (DNS) is applied to enhance local search capabilities in the onlooker bee phase; (4) the new food sources generation strategy was proposed to reduce the blindness in the scout bee phase. Finally, this paper carried out a series of comparative experimental studies, including the comparison before and after algorithm improvement, and the comparison between the improved algorithm with MOPSO, MODE and NSGA-II. The results demonstrate that the IABC can achieve a better performance for solving the MLFJSP.
•Multi-objective Low-carbon flexible job shop scheduling problem is studied.•Makespan, machine loading and total carbon emission objectives are considered.•An improved artificial bee colony (IABC) algorithm is proposed.•A total of 15 benchmarks are used to test the performance of the IABC.
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This ...can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).
Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm ...for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the
K
-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.
•The study presents a simulation-based optimization method for building performance.•The building energy consumption and thermal comfort are optimized simultaneously.•EnergyPlus is used as the ...building energy simulation program.•The developed method is applied to a case study building in four climates of Iran.•The final optimum design is selected using TOPSIS decision-making approach.
The aim of this paper is to present a powerful simulation-based multi-objective optimization of building energy efficiency and indoor thermal comfort to obtain the optimal solutions of the comfort-energy efficient configurations of building envelope. The optimization method is developed by integrating a multi-objective artificial bee colony (MOABC) optimization algorithm implemented in MATLAB with EnergyPlus building energy simulation tool. The proposed optimization approach is applied to a single office room; and the building parameters, including the room rotation, window size, cooling and heating setpoint temperatures, glazing and wall material properties are considered as decision variables. In the present study, single-objective and multi-objective optimization analyses of the total annual building electricity consumption and the Predicted Percentage of Dissatisfied (PPD) are investigated to bring down the total energy cost as well as the thermal discomfort in four major climate regions of Iran, i.e. temperate, warm-dry, warm-humid and cold ones. In the results part, the achieved optimal solutions are presented in the form of Pareto fronts to reveal the mutual impacts of variables on the building energy consumption and the thermal discomfort. Finally, the ultimate optimum solution on the Pareto fronts are selected by TOPSIS decision-making method and the results of double-objective minimization problem are compared with the single-objective ones as well as the base design. The results of double-objective optimization problem indicate that in different climates, even though the total building electricity consumption increases a bit about 2.9–11.3%, the PPD significantly decreases about 49.1–56.8% compared to the baseline model. In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution.
Clustering is the most common approach to achieve energy efficiency in wireless sensor networks. The existing clustering techniques exhibit some drawbacks which limit their usage for practical ...networks. First, cluster heads are typically selected among all sensor nodes within the network, and consequently, unbalanced clusters may be generated. Second, the controllable parameters are defined manually. Third, the protocol is not adjusted and tuned based on application specifications. In this paper, we propose an adaptive fuzzy clustering protocol (named LEACH-SF), in order to overcome the mentioned drawbacks. In LEACH-SF, fuzzy c-means algorithm is used to cluster all sensor nodes into balanced clusters, and then appropriate cluster heads are selected via Sugeno fuzzy inference system. The fuzzy inputs of the Sugeno fuzzy inference system include the residual energy, the distance from sink, and the distance from cluster centroid. Unlike the existing fuzzy-based routing protocols in which the fuzzy rule base table is defined manually, we utilize artificial bee colony algorithm to adjust the fuzzy rules of LEACH-SF. The fitness function of the algorithm is defined to prolong the network lifetime, based on the application specifications. In other words, LEACH-SF not only prolongs the lifetime, but also is applicable to any kind of application. Simulations over 10 heterogeneous wireless sensor networks show that LEACH-SF outperforms the existing cluster-based routing protocols.
•A Sugeno fuzzy clustering algorithm is presented for wireless sensor networks.•FCM algorithm is utilized to form balanced clusters over the network.•Artificial bee colony algorithm is utilized to optimize the Sugeno fuzzy rules.•Proposed Sugeno model can be adaptively tuned via ABC for any application.
In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there ...are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.
Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short ...circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I-V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.
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