Layout optimization of fiber Bragg grating strain sensor network based on modified artificial fish swarm algorithm

Huang, Jiwei; Zeng, Jie; Bai, Yufang; Cheng, Zhuming; Feng, Zhenhui; Qi, Lei; Liang, Dakai

doi:10.1016/j.yofte.2021.102583

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…The artificial fish corresponds to the optimal solution of the optimization problem, the water area corresponds to the solution space of the optimization problem, and the food concentration corresponds to the objective function of the optimization problem. Some parameters of AFSA are defined as follows [20]. There are N artificial fishes in a D-dimensional space.…”

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

“…(1) Preying behavior: assuming that the current state of the artificial fish is X, a new state X next is obtained firstly according to Equation (20) and Equation (21). Then, the food concentration functions Y and Y next for X and X next are calculated, respectively.…”

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

“…If Y next is superior to Y, X will be moved to X next according to Equation (21). Otherwise, the new state of artificial fish will continue to be obtained according to Equation (20) and Equation (21). If the artificial fish acquires new states more than L times and Y next is still worse than Y, the artificial fish performs random behavior…”

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

“…(a) Preying behavior: assuming that the current state of the artificial fish is X, a new state X next is obtained firstly according to Equation (20) and Equation (21). Since the food concentration function is no longer a value, but a vector, the basis for determining whether X moves to X next is whether FðX next Þ dominates FðXÞ.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Since the food concentration function is no longer a value, but a vector, the basis for determining whether X moves to X next is whether FðX next Þ dominates FðXÞ. If FðX next Þ dominates FðXÞ, then, X moves to X next , otherwise continue generating a new solution according to Equation (20) and Equation (21) until exceeding L times, and then, performs random behavior (b) Following behavior: assuming that the current state of the artificial fish is X, the artificial fish searches for nondominated solutions within its perceived range according to the Pareto dominance rule, then, calculate the congestion distance of each artificial fish is calculated, and the solution X gbest with the largest congestion distance is obtained. If DisðX gbest Þ < D isðXÞ, X moves to X gbest according to Equation ( 22), otherwise the swarming behavior is performed (c) Swarming behavior: assuming that the current state of the artificial fish is X, the central position of the artificial fish within the perceived range is X c .…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

See 4 more Smart Citations

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Yang

Liu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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The single-stand reversible cold rolling mill is important equipment in the production of steel strips. The rolling schedule is the core technological content in the strip production of the single-stand reversible cold rolling mill. The scientific rolling schedule is the fundamental guarantee for the production capacity of the rolling mill, product quality, accuracy, shape quality, energy saving, and consumption reduction. This paper takes the dynamic rolling process of single-stand cold rolling as the research object, the purposes of increasing production capacity, saving energy, and reducing consumption are achieved by optimizing the rolling schedule. Based on the study of the mechanism model and the analysis of a large number of field measured data, a slice of mathematical models of the rolling process suitable for engineering calculation are proposed, and a few objective functions suitable for the single-stand reversible cold rolling process are designed. On this basis, the artificial fish swarm algorithm is improved into a multiobjective optimization algorithm for the optimization of rolling schedule, and the optimal rolling load distribution scheme is obtained. Finally, the optimization method of rolling schedule proposed in this paper is applied to the actual rolling production. The results show that the proposed method can improve productivity and save energy compared with the empirical rolling schedule, and the feasibility and validity of the proposed algorithm are verified.

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Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Section: Multiobjective Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Yang

Liu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Research on Task Allocation Method of Mobile Swarm Intelligence Perception Based on Hybrid Artificial Fish Swarm Algorithm

et al. 2021

Business Intelligence and Information Technology

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In this study, the task allocation mechanism in mobile swarm intelligence perception is studied and analyzed. Considering how to allocate tasks under specified time constraints and optimize for the goal, a hybrid artificial fish swarm algorithm is proposed by using swarm intelligence algorithm. The inertia index of particle swarm optimization algorithm is pulled into the typical artificial fish swarm algorithm, which is proved by simulation experiments. The hybrid artificial fish swarm algorithm greatly improves the convergence speed, avoids the shortage that the artificial fish swarm algorithm often stop when it obtains the local optimization, and this make the algorithm obtains the effect of global optimization.

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Artificial Fish Swarm Algorithm for the Optimization of a Benchmark Set of Functions

Peraza

Ochoa²,

Amador³

et al. 2022

Studies in Computational Intelligence

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Layout optimization of fiber Bragg grating strain sensor network based on modified artificial fish swarm algorithm

Cited by 12 publications

References 23 publications

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Research on Task Allocation Method of Mobile Swarm Intelligence Perception Based on Hybrid Artificial Fish Swarm Algorithm

Artificial Fish Swarm Algorithm for the Optimization of a Benchmark Set of Functions

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