An Enhanced Discrete Bees Algorithm for Resource Constrained Optimization Problems

Abstract

In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of project scheduling problems and reduces the number of parameters to be tuned. The proposed EDBA is tested on well-known benchmark problem instance sets from Project Scheduling Problem Library (PSPLIB) and compared with other approaches from the literature. The promising computational results reveal the effectiveness of the proposed approach for solving the RCPSP problems of various scales.

An integrated hybrid metaheuristic model for the constrained scheduling problem

“…An exhaustive comparison of our model has been done with the best available metaheuristics in literature. These include models involving genetic algorithms [5], ACO [2], [11], [12], PSO [15], [19], BA [16], [18], and other such metaheuristics. To be able to present a fair comparison, the TLBO model too has been tested over 1,000 and 5,000 schedules for 30 independent runs for lower and medium size test instances i.e.…”

“…Ziarati et al [16] suggested three different variations of the bee algorithms. Some discretized permutationbased bee algorithm techniques have been proposed by [17], [18]. Research based on hybridization of BA and PSO has also been suggested based on experiments carried out in [19].…”

Generating Honeyword Based on A Proposed Bees Algorithm

“…The latter step is similar to exploration, and the bees in this status are referred to as scouts [29], [30]. Scout bees that discovered the better flower patches are considered elite bees have a specific dancing habit for positive feedback, and this waggle dance is vital for the exploitation of good food sources [31], [32]. The waggle dance will attract colony bees to the selected flower patches, here the bees will regard as recruits [33], [34].…”

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

“…Metaheuristicalgorithmscanfindglobaloptimalsolutionsfortheproblemswheretherearemanylocal solutionsduetotheirrandomnature.Thesereasonshaveledtoextensiveuseofsuchalgorithmsin solvingvariousoptimizationproblemsandhavebeensuccessfullyappliedtomonoandmulti-objective complicatedproblemsofscientificandengineeringcomputing.Inthelastdecade,researchershave carriedoutextensivestudiesonmetaheuristicalgorithmssuchasparticleswarmoptimization(PSO) (Erdogmus, 2018) harmony search (HS) (Abdel-Raouf & Metwally, 2013), artificial bee colony (ABC) (Karaboga,Gorkemli,Ozturk&Karaboga,2012),cuckoosearch(CS)fireflyalgorithm(FA) (Fister, Yang, Fister, & Fister, 2013), imperialist competitive algorithm (ICA) (Liu, Su, & Chiu, 2013),teaching-learning-basedoptimization(TLBO) (Rao,2015),differentialevolutionalgorithm (DE) (Das&Suganthan,2011),SocialSpiderOptimization(SSO) (Cuevas,Cienfuegos,Zaldívar, &Pérez-Cisneros,2013)andbiogeography-basedoptimizer(BBO) (Ma&Simon,2017).Besides, manymetaheuristicalgorithmshavebeenimprovedtosolvereal-worldoptimizationproblemssuch asevolutionaryalgorithmsformobilemulti-hopAdHocnetworkoptimizationproblems (Reina,et al, 2016), a decomposition-based multi-objective firefly algorithm developed for RFID network planning (Zhaoetal.,2017)andanenhancedbeesalgorithmforresourceconstrainedoptimization problems (Nemmich, Debbat & Slimane, 2019). Based on the "no free lunch" theorem (NFL) (Koehler,2007),thereisnooptimizationalgorithmthatworkswellonalloptimizationproblems.…”