Discrete Artificial Bee Colony Research Articles

Endmember Extraction of Hyperspectral Remote Sensing Images Based on an Improved Discrete Artificial Bee Colony Algorithm and Genetic Algorithm

Aiming at improving the performance of the endmember extraction problem in hyperspectral images, a new extraction method based on discrete hybrid artificial bee colony algorithm and genetic algorithm (DABC_GA) is proposed. By analyzing the characteristic of the problem, each dimension of candidate solution is a discrete and exclusive integer. Then we employ an optimization method with integral coding. By inheriting the strong exploration ability of the traditional artificial bee colony algorithm (ABC), we propose a discrete ABC which could quickly obtain more valuable endmembers combinations in the early stage. Then we select some outstanding results of DABC as the potential solutions of GA, which is adopted as another optimization tool in the later stage of iteration. The concept of complementary sets is proposed in the cross and mutation operators to guarantee the diversity and completeness of solutions. Meanwhile, the greedy strategy is adopted to ensure that the favorable potential solutions are not discarded. Compared with conventional extraction algorithms in simulated and real hyperspectral remote sensing data, the experimental results show the validity of our proposed algorithm.

The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance

Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement.

Power System Restoration Planning Strategy Based on Optimal Energizing Time of Sectionalizing Islands

Common power system restoration planning strategy is based on a ‘build up’ approach, where a blackout system is sectionalized among several islands for parallel restoration prior to resynchronization. In order to speed up the resynchronization of the islands, each island must have similar energizing times. However, there is a huge number of possible combinations of islands that can be formed. Thus, this paper proposes a method to determine optimal islands that have similar energizing times. The method involves identifying transmission lines that should not be connected to form the islands. The proposed method is based on the combination of heuristic and discrete optimization methods. The heuristic technique is proposed to find initial solution that is close to the optimal solution. This solution will guide the optimization technique, which is the discrete Artificial Bee Colony optimization method, to find the optimum solution. The proposed method also considers restoration constraints including black start generator availability, load-generation balance, and the maintenance of acceptable voltage magnitude within each island. The proposed method is validated via simulation using IEEE 39, 118-bus and 89-bus European systems. The advantage of the proposed method in terms of restoration time is demonstrated through a comparison with other literature.

PTS based on DisABC algorithm for PAPR reduction in OFDM systems

A novel discrete artificial bee colony algorithm-based partial transmit sequences (DisABC-PTS) scheme is proposed to reduce peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing signals. Compared to ABC algorithm-based PTS scheme (ABC-PTS) proposed previously, the DisABC-PTS scheme works directly in discrete space, which has a high computational efficiency. Simulation results indicate that DisABC-PTS scheme can obtain lower PAPR than existing PTS schemes and has a low computational complexity.

A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems

A blocking lot-streaming flow shop (BLSFS) scheduling problem is to schedule a number of jobs on more than one machine, where each job is split into a number of sublots while no intermediate buffers exist between adjacent machines. The BLSFS scheduling problem roots from traditional job shop scheduling problems but with additional constraints. It is more difficult to be solved than traditional job shop scheduling problems, yet very popular in real-world applications, and research on the problem has been in its infancy to date. This paper presents a hybrid multi-objective discrete artificial bee colony (HDABC) algorithm for the BLSFS scheduling problem with two conflicting criteria: the makespan and the earliness time. The main contributions of this paper include: (1) developing an initialization approach using a prior knowledge which can produce a number of promising solutions, (2) proposing two crossover operators by taking advantage of valuable information extracted from all the non-dominated solutions in the current population, and (3) presenting an efficient Pareto local search operator based on the Pareto dominance relation. The proposed algorithm is empirically compared with four state-of-the-art multi-objective evolutionary algorithms on 18 test subsets of the BLSFS scheduling problem. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of several widely-used performance metrics.

Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD) program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC) algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS). Specifically, the assembly sequences of three-dimensional (3D) parts prepared in the computer-aided design (CAD) software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

An Efficient Optimization Algorithm for Resource-Constrained Steelmaking Scheduling Problems

In this paper, we address the steelmaking scheduling problems with multiple constrained resources. The objective is to minimize the maximum completion time. We consider the continuous casting constraint and resource constraints simultaneously. Based on the artificial bee colony (ABC) algorithm, we propose several heuristics and develop a discrete ABC (DABC) algorithm for the considered problem. In the proposed algorithm, a two-phase-based encoding mechanism is presented to create different searching abilities during different stages of evolution. Next, a decoding method considering the resource constraints is designed. A local search procedure is developed to utilize the two-phase encoding mechanism efficiently. Then, a global search heuristic is investigated to enhance the ability to escape from the local best. Finally, we test the proposed DABC algorithm by running it on sets of instances that are randomly generated based on realistic steelmaking production system. After detailed comparisons and statistical analysis, the competitive performance of the proposed DABC algorithm is verified.

Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic

Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic

Dual neighbourhood discrete artificial bee colony algorithm for the convex polygons packing problem with constraints of equilibrium

For the optimal packing problem of convex polygons in a circular container with performance constraint, a hybrid algorithm which combines an improved ripple exploratory heuristic algorithm (IREHA) with dual-neighbourhood discrete artificial bee colony algorithm (DDABC) is proposed. IREHA improves search efficiency of the original REHA. Because of the excellent performance of artificial bee colony algorithm, which can not be directly used to optimise the discrete packing order, DDABC based on discrete dual neighbourhood structure is designed to combine with IREHA to form hybrid algorithm. The numerical experiments show that the hybrid algorithm is very effective.

Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic

Data clustering is a method of partitioning data into different groups pursuant to some similarity or dissimilarity measure. Nowadays, several different technics are invented and introduced for data clustering such as heuristics and meta-heuristics. Many clustering algorithms fail when dealing with multi-dimensional data. In this research, we proposed an innovative fuzzy method with improved discrete artificial bee colony (ID is ABC) for data clustering called FID is ABC. The D is ABC is a new version of artificial bee colony (ABC) that first introduced to sort out the uncapacitated facility location problem (UFLP) and improved by the efficient genetic selection to solve dynamic clustering problem. The performance of our algorithm is evaluated and compared with some well-known algorithms. The results show that our algorithm has better performance in comparison with them.

Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem

Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem

Dual neighbourhood discrete artificial bee colony algorithm for the convex polygons packing problem with constraints of equilibrium

For the optimal packing problem of convex polygons in a circular container with performance constraint, a hybrid algorithm which combines an improved ripple exploratory heuristic algorithm (IREHA) with dual-neighbourhood discrete artificial bee colony algorithm (DDABC) is proposed. IREHA improves search efficiency of the original REHA. Because of the excellent performance of artificial bee colony algorithm, which can not be directly used to optimise the discrete packing order, DDABC based on discrete dual neighbourhood structure is designed to combine with IREHA to form hybrid algorithm. The numerical experiments show that the hybrid algorithm is very effective.

An effective computational approach based on XFEM and a novel three-step detection algorithm for multiple complex flaw clusters

This paper presents an effective computational approach comprised of forward and inverse analyses for detection of multiple complex flaw clusters in elastic solids. A three-step detection strategy is introduced for inverse analysis, whereas extended finite element method (XFEM) is adopted for forward analysis. The use of XFEM is to avoid re-meshing during the change of flaw geometries. The three-step detection strategy involves: firstly, an optimization method that couples an improved discrete artificial bee colony algorithm and hierarchical clustering analysis (IDABC-HCA) is used to capture subdomains containing flaws with limited measure points in the global domain; secondly, additional measure points are introduced locally within each captured subdomain, where the number of flaws and the rough geometry of each flaw are quickly determined with the IDABC-HCA; finally, true geometries of flaws are obtained on the basis of the rough geometries by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. To save computational time, “Queue and Kill” method is proposed to actively identify and eliminate the improper candidate flaws and/or flaw clusters. Three numerical examples of multiple flaw detection that include simple and complex flaw geometries are analyzed. The results demonstrate that the proposed approach can effectively detect multiple complex flaw clusters without prior information of the flaw number.

A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem

Artificial bee colony (ABC) algorithm, which has explicit strategies to balance intensification and diversification, is a smart swarm intelligence algorithm and was first proposed for continuous optimization problems. In this paper, a hybrid discrete ABC algorithm, which uses acceptance criterion of threshold accepting method, is proposed for Traveling Salesman Problem (TSP). A new solution updating equation, which can learn both from other bees and from features of problem at hand, is designed for the TSP. Aiming to enhance its ability to escape from premature convergence, employed bees and onlooker bees use threshold acceptance criterion to decide whether or not to accept newly produced solutions. Systematic experiments were performed to show the advantage of the new solution updating equation, to verify the necessity of using non-greedy acceptance strategy for keeping sufficient diversity, to compare different selection schemes for onlooker bees, and to analyze the contribution of scout bee. Comparison experiments performed on a wide range of benchmark TSP instances have shown that the proposed algorithm is better than other ABC-based algorithms and is better than or competitive with many other state-of-the-art algorithms.

Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance (IGD) and spacing (SP), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively.

Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems

Resource fair allocation is a challenging problem in heterogeneous cloud computing systems, both in real-life problems and for scientific research purposes. However, it is an NP-hard problem and solutions obtained by existing heuristic algorithms have a significant gap up to the optimal solutions. Motivated by this fact, we propose three swarm optimization algorithms: discrete artificial bee colony, discrete artificial fish swarm, and discrete shuffled frog leaping. In addition, we investigate how to utilize the impact of search behavior to improve the performance of the algorithm, and we design the self-adaptive parameter settings to balance between the exploitation and exploration of the algorithm. Furthermore, we propose a heuristic algorithm to generate a good initial solution. Compared with some algorithms from the literature, the simulation results show that our proposed algorithms can maximize the global dominant share fairly and increase the resource utilization, and they are highly adaptable to different situations.

A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

Abstract To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP.

Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times

The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D , to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.

A hybrid artificial bee colony for optimizing a reverse logistics network system

This paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm for solving the location allocation problem in reverse logistics network system. In the proposed algorithm, each solution is represented by two vectors, i.e., a collection point vector and a repair center vector. Eight well-designed neighborhood structures are proposed to utilize the problem structure and can thus enhance the exploitation capability of the algorithm. A simple but efficient selection and update approach is applied to the onlooker bee to enhance the exploitation process. A scout bee applies different local search methods to the abandoned solution and the best solution found so far, which can increase the convergence and the exploration capabilities of the proposed algorithm. In addition, an enhanced local search procedure is developed to further improve the search capability. Finally, the proposed algorithm is tested on sets of large-scale randomly generated benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HDBAC algorithm is shown against several efficient algorithms from the literature.