Hybrid Bees Algorithm Research Articles

BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs

In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive performance compared with some algorithms extracted from the literature.

Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm

Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in both forward and backward sides. Furthermore, the BA was used to optimize the learning rate factor in the fully connected layer. In this study, artificial porosity images were used for testing the algorithms; since the input data were images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images to feed into the LSTM for predicting the percentage of porosity in the sequential layers of artificial porosity images that mimic real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. Applying a Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) yielded a porosity prediction accuracy of 93.17%. Although using Bayesian Optimization (BO) to optimize the LSTM parameters mentioned previously did not improve the performance of the LSTM, as the prediction accuracy was 93%, adding the BA to optimize the same LSTM parameters did improve its performance in predicting the porosity, with an accuracy of 95.17% where a hybrid Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM) was used. Furthermore, the hybrid BA-CNN-LSTM algorithm was capable of dealing with classification problems as well. This was shown by applying it to Electrocardiogram (ECG) benchmark images, which improved the test set classification accuracy, which was 92.50% for the CNN-LSTM algorithm and 95% for both the BO-CNN-LSTM and BA-CNN-LSTM algorithms. In addition, the turbofan engine degradation simulation numerical dataset was used to predict the Remaining Useful Life (RUL) of the engines using the LSTM network. A CNN was not needed in this case, as there was no feature extraction for the images. However, adding the BA to optimize the LSTM parameters improved the prediction accuracy in the testing set for the LSTM and BO-LSTM, which increased from 74% to 77% for the hybrid BA-LSTM algorithm.

Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks

This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.

A green transportation location-inventory-routing problem by dynamic regional pricing

Non-uniform distribution of customers in a region and variation of their maximum willingness to pay at distinct areas make regional pricing a practical method to maximize the profit of the distribution system. By subtracting the classic objective function, which minimizes operational costs from revenue function, profit maximization is aimed. A distribution network is designed by determining the number of trucks to each established distribution center, allocating customers in routes, and inventory levels of customers. Also, environmental impacts, including fuel consumption and CO2 emission, aimed to be minimized. So, a new quadratic mixed-integer programming model is presented for the Green Transportation Location-Inventory-Routing Problem integrated with dynamic regional pricing problem (GTLIRP+DRP). The model is applied to the real case study, to show its competent application. To tackle this problem, a Hybrid Bees Algorithm (HBA) is developed and verified by the genetic algorithm. Finally, managers suggested using HBA that achieves better solutions in the less computational time.

A honeybees-inspired heuristic algorithm for numerical optimisation

Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.

Three effective metaheuristics to solve the multi-depot multi-trip heterogeneous dial-a-ride problem

The Heterogeneous Dial-a-Ride Problem (HDARP) is an important problem in reduced mobility transportation. Recently, several extensions have been proposed towards more realistic applications of the problem. In this paper, a new variant called the Multi-Depot Multi-Trip Heterogeneous Dial-a-Ride Problem (MD-MT-HDARP) is considered. A mathematical programming formulation and three metaheuristics are proposed: an improved Adaptive Large Neighborhood Search (ALNS), Hybrid Bees Algorithm with Simulated Annealing (BA-SA), and Hybrid Bees Algorithm with Deterministic Annealing (BA-DA). Extensive experiments show the effectiveness of the proposed algorithms for solving the underlying problem. In addition, they are competitive to the current state-of-the-art algorithm on the MD-HDARP.

BFO: a hybrid bees algorithm for the multi-level capacitated lot-sizing problem

This paper presents a hybrid heuristic based on the bees algorithm combined with the fix-and-optimize heuristic to solve the multi-level capacitated lot-sizing problem. The bees algorithm can be used as a new method to determine the sequence in which to apply the partition in the fix-and-optimize approach. This new manner of choosing the partition adds diversity to the solution pool and yields different local optima solutions after some iterations. The bees-and-fix-and-optimize (BFO) algorithm attempts to avoid these local optima by performing random search in accordance with the concept of bees algorithm. The BFO has yielded good results for instances from the literature and, in most cases, the results are superior to the best results provided by approaches presented in recent literature. They show that this construction concept is advantageous and illustrate the efficiency of hybrid methods composed of matheuristics and metaheuristics. Furthermore, the BFO approach is a general-purpose heuristic that can be applied to solve other types of production planning problems.

Hybrid Bees Algorithm to Solve Aircraft Landing Problem


 
 
 
 In this paper, we study the aircraft landing problem (ALP), which considered as one of a combinatorial optimization problems, in a single runway case. We present in the first part, a mathematical formulation of the problem with a linear objective function. In the second part, we consider the static case of the problem where all data are known in advance. We present a new heuristic for scheduling static case of aircraft landing; this heuristic is incorporated into Bees algorithm to solve this problem.
 
 
 

Modeling and solving mixed-model assembly line balancing problem with setups. Part II: A multiple colony hybrid bees algorithm

This paper is the second one of the two papers entitled “Modeling and Solving Mixed-Model Assembly Line Balancing Problem with Setups”, which deals with the mixed-model assembly line balancing problem of type I (MMALBP-I) with some particular features of the real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. Due to the complex nature of the problem, we tackled the problem with bees algorithm (BA), which is a relatively new member of swarm intelligence based meta-heuristics and tries to simulate the group behavior of real honey bees. However, the basic BA simulates the group behavior of real honey bees in a single colony; we aim at developing a new BA, which simulates the group behavior of honey bees in a single colony and between multiple colonies. The multiple colony type of BA is more realistic than the single colony type because of the multiple colony structure of the real honey bees; each colony represents the honey bees living in a different hive and is generated with a different heuristic rule. The performance of the proposed multiple colony algorithm is tested on 36 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with single colony algorithms in terms of solution quality and computational times. Computational results indicate that the proposed multiple colony algorithm has superior performance. Part II of the paper also presents optimal solutions of some problems provided by MILP model developed in Part I.

Solving large-scale SONET network design problems using bee-inspired algorithms

In the past years, the number of users of Internet-based applications has exponentially increased and consequently the request for transmission capacity or bandwidth has significantly augmented. When managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. In this paper, we consider two problems that arise in the design of optical telecommunication networks, namely the SONET Ring Assignment Problem (SRAP) and the Intraring Synchronous Optical Network Design Problem (IDP), known to be NP-hard. In SRAP, the objective is to minimise the number of rings (i.e., DXCs). In IDP, the objective is to minimise the number of ADMs. Both problems are subject to a ring capacity constraint. To solve these problems, we propose two bee-inspired algorithms: Hybrid Artificial Bee Colony and Hybrid Bees Algorithm. We hybridise the basic form of these algorithms with local search, in order to refine newly constructed solutions. We also perform comparisons with other algorithms from the literature and use larger instances. The simulation results verify the effectiveness and robustness of the proposed algorithms.

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR SOLVING CONTAINER LOADING PROBLEMS

The globalization of supply chains and rising fuel costs are forcing container carriers both to minimize the number of trips and to maximize available container space. This makes container loading (CL) a critical process especially in real‐life applications. Container loading (CL), which is a difficult problem to be solved, has many applications in container transportation and distribution industries. This article presents a container loading support system (CLSS). The proposed CLSS composes of three main components, including a hybrid Bees Algorithm as the main computational algorithm, the graphical user interface (GUI) and a simulation program. The aim of the designed system is to make the packing pattern more visible to the user in order to simplify the loading process. An illustrative example ‐a CL problem from literature ‐ is also provided to introduce the operation of the system and to prove its efficiency.