A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Ma, Lianbo; Huang, Min; Zhang, Hao; Chen, Hanning

doi:10.1016/j.asoc.2017.04.011

Cited by 38 publications

(4 citation statements)

References 41 publications

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“…Each problem has two objectives. The Inverse Generational Distance (IGD) metric is used as the testing standard [37] [38]. The iteration in one run is 1000; Each test function was run 30 times, and the average values and standard deviation of the test function were recorded in the Table 1.…”

Section: Test Functions and Experimental Setupmentioning

confidence: 99%

Multi-indicators Brain Storm Optimization Algorithm for Multi-objective Optimization Problems

Zhang¹,

Yang²,

Zhang³

et al. 2019

dtcse

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The brains storming optimization (BSO) is a novel swarm intelligence algorithm, which is inspired from the intelligence of the human brainstorming process. In BSO, the convergent operation and divergent operation are implemented to drive the evolution of individuals towards a global optimal region in the single-objective space. In order to extend BSO to resolve multi-objective optimization problems, this paper presents a multi-objective brainstorming algorithm based on multiple indicators, called MIBSO. In MIBSO, the two indicators are used to measure the dispersion and convergence degrees of the obtained solutions, respectively. Experimental results show the effectiveness and efficiency of the proposed algorithm.

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Section: Test Functions and Experimental Setupmentioning

confidence: 99%

Multi-indicators Brain Storm Optimization Algorithm for Multi-objective Optimization Problems

Zhang¹,

Yang²,

Zhang³

et al. 2019

dtcse

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“…We mathematically formulate this problem as a multiobjective optimization model with two different goals of minimizing total resource consumption cost and operating cost, respectively. Similar to most existing works, [10][11][12][13][14]16,[19][20][21][22][23][24][25][26][31][32][33] we transform the multiobjective optimization problem into a single objective optimization problem and base on Dijkstra algorithm to design a heuristic service deployment approach to solve it.…”

Section: Related Workmentioning

confidence: 99%

Dynamic network service deployment across multiple SDN domains

Zhang

Wang

Dong

et al. 2019

Trans Emerging Tel Tech

Self Cite

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The integration of software defined networking (SDN) and network function virtualization (NFV) is a promising approach to support flexible service deployment and provisioning. In particular, NFV enables a network service to be represented as one network service function chain, which is composed of several virtual network functions in a specific order. However, with regard to service provisioning in multidomain SDN networks, virtual network functions are deployed in several different SDN domains due to limited resource capacity of each SDN domain. It results in the network service deployment complexity increase and causes additional operating cost. On the other hand, most current network service deployment proposals are limited to static user request scenarios. How to effectively deploy network service across multiple SDN domains to satisfy dynamic service demands of end users is a challenging problem. To address the aforementioned challenge, in this paper, we study the problem of dynamic network service deployment across multiple SDN domains. Specifically, we first design a novel service deployment framework to support dynamic deployment and optimization of virtual network function in multidomain SDN networks. Then, we formulate the problem as a multiobjective optimization problem with the target of jointly minimizing resource consumption cost and operating cost. Furthermore, we transform the multiobjective optimization problem into a single objective optimization model and present an efficient heuristic service deployment approach to solve it by leveraging Dijkstra algorithm. The simulation results demonstrate that the proposed service deployment method is efficient and outperforms comparison methods in terms of service rejection ratio, resource cost, and operating cost.

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“…The canonical ABC algorithm is proposed by Karaboga in 2005 for parameter optimization, simulating the foraging behavior of a bee colony. Recently, it is introduced to solve unimodal and multi-modal numerical optimization problems [30][31][32][33]. In ABC, there are three kinds of bees including [34,35]: employed bees, onlooker bees and scout bees.…”

Section: Standard Abc Algorithmmentioning

confidence: 99%

Resolving Micro-grid Energy Optimization Using Multi-objective Artificial Bee Colony Algorithm

Tung¹,

Shi²

2019

dtcse

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Aiming at the micro-grid energy scheduling, this paper proposes a bio-objective optimization model of the economic and emission dispatch, where two conflicting objectives of the fuel costs and pollution emission are constructed. In order to resolve this model, a multi-objective artificial bee colony (MOABC) algorithm is proposed by using the non-dominated sorting and best-candidate strategies. Especially, an external archive is used to preserve the non-dominated solutions and the best-candidate strategy is to select the best non-dominated solutions to maintain the diversity of Pareto optimal Solutions (PS). An experiment is conducted via comparing IBEA and BC-MOABC on a set of test instances. The results show that the BC-MOABC obtains better results in terms of the convergence on PF. Furthermore, we apply BC-MOABC to resolve the micro-grid energy scheduling optimization problems. Experimental results show that the proposed model and algorithm can reasonably arrange the distributed generations and reduce the system cost and gas emissions.

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A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Cited by 38 publications

References 41 publications

Multi-indicators Brain Storm Optimization Algorithm for Multi-objective Optimization Problems

Multi-indicators Brain Storm Optimization Algorithm for Multi-objective Optimization Problems

Dynamic network service deployment across multiple SDN domains

Resolving Micro-grid Energy Optimization Using Multi-objective Artificial Bee Colony Algorithm

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