A multi-population harmony search algorithm with external archive for dynamic optimization problems

The optimal mapping of tasks to the processors is one of the challenging issues in heterogeneous computing systems. This article presents a task scheduling problem in distributed systems using discrete particle swarm optimization (DPSO) algorithm with various neighborhood topologies. The DPSO is a recent metaheuristic population‐based algorithm. In DPSO, the set of particles in a swarm flies through the N‐dimensional search space by learning from both the personal best position and a neighborhood best position. Each particle inside the swarm belongs to a specific topology for communicating with neighboring particles in the swarm. The neighborhood topology affects the performance of DPSO significantly, because it determines the rate at which information transmits through the swarm. The proposed DPSO algorithm works on dynamic topology that is binary heap tree for communication between the particles in the swarm. The performance of the proposed topology is compared with other topologies such as star, ring, fully connected, binary tree, and Von Neumann. The three well‐known performance measures such as Makespan, mean flow time, and reliability cost are used for the comparison of the proposed topology with other neighborhood topologies. Computational simulation results indicate that the performance of DPSO algorithm has shown significant improvement with binary heap tree topology used for communication among the particles in the swarm.

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“…This section presents the comparison of computation complexity (Turky & Abdullah, ) of the proposed HIDPSO and the existing DPSO algorithms.…”

Section: Computation Complexitymentioning

confidence: 99%

Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

Sarathambekai

Umamaheswari

2017

Computational Intelligence

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“…In this paper, the size of external archive is equal to the population size and controlled by the crowding distance (CD) [18]. The obtained non-dominated solutions are converted to continuous representation as it is mentioned in Section 4.1, then considered as the pbest.…”

Section: The Regeneration Of the Particlesmentioning

confidence: 99%

An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems

Xiao

Wang

et al. 2017

Entropy

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Abstract:In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP) is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy information graph (DHIG). Then, combining the characteristic of the disassembly sequence, an entropy-based adaptive hybrid particle swarm optimization algorithm (AHPSO) is presented. In this algorithm, entropy is introduced to measure the changing tendency of population diversity, and the dimension learning, crossover and mutation operator are used to increase the probability of producing feasible disassembly solutions (FDS). Performance of the proposed methodology is tested on the primary problem instances available in the literature, and the results are compared with other evolutionary algorithms. The results show that the proposed algorithm is efficient to solve the complex DLBP.

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“…Another improved version of SPSO (rSPSO) was developed by integrating a least square regression method [2]. Recently, a multi-population harmony search algorithm was proposed [40], where a harmony search method [13] is used for each population to locate peaks. The best individuals of converged populations are kept to replace redundant ones.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima

Nguyễn

Yang

et al. 2016

IEEE Trans. Evol. Computat.

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A multi-population harmony search algorithm with external archive for dynamic optimization problems

Cited by 96 publications

References 29 publications

Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems

An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima

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