A resource-constrained project scheduling problem (RCPSP) is one of the most famous intractable NP-hard problems in the operational research area. To effectively solve the RCPSP, we propose a hybrid approach by integrating artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. ABC-PSO is devised based on embedded ABC-PSO (EABC-PSO) and sequential ABC-PSO (SABC-PSO) strategies. In both strategies, bees in the ABC process are entitled to learning capacity from the best local and global solutions in terms of the PSO concept. Subsequently, the updates of solutions are premeditated with crossover and insert operators together with double justification methods. Computational results obtained from the tests on benchmark sets show that the proposed ABC-PSO algorithm is efficient in solving RCPSP problems, demonstrating clear advantages over the pure ABC algorithm, the PSO algorithm, and a number of listed heuristics.
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Benchmark sets (graphs) show that the proposed ABC –PSO algorithm
#Hybridization, #Artificial, #Bee, #Colony, #Particle, #Swarm, #Optimization, #Algorithm, #Resource, #Project, #Scheduling, #Intractable, #Operational, #Research, #Integrating, #Embedded, #Sequential, #Strategies, #Capacity, #Local, #Global, #Solution, #Update, #Premeditated, #Crossover, #Insert, #Operators, #Double, #Justification, #Results, #Benchmark, #Number, #Listed, #Heuristics,
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