The force law design of artificial physics optimization for starting population selection for GSAT

“…The artificial physics optimization (APO) algorithm simulates Newton's second law and assigns mass properties to individuals. The algorithm has some similarities with the PSO algorithm, and its performance can be compared with or even surpasses the classical optimization algorithms [22,23]. The core of the APO algorithm contains the selection of the non-dominated set, the calculation of the mass function, and the virtual force.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion

Sun

Fan

et al. 2022

IEEE Access

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The elements of the population of the artificial physics optimization (APO) algorithm are assigned mass, velocity, and displacement attributes. It is a new type of heuristic algorithm, but it still has defects such as low efficiency of non-dominated solution selection and unbalanced search capacity of global and local. This paper introduces the mechanism of improved fast non-dominant sorting and partitionguided individual evolution into the APO algorithm to overcome this imperfection. An improved multiobjective artificial physics optimization algorithm based on fast non-dominated sorting (IFNS-MOAPO) is proposed, which is the result of integrating numerous strategies into the APO algorithm. Firstly, an improved fast non-dominated sorting strategy is introduced. This strategy can increase the efficiency of selecting non-dominated solutions and decrease the running time of the algorithm. Secondly, the mechanism of individual evolution guided by partition is proposed. For individuals in infeasible and feasible domains, different mass functions and virtual force calculation rules are adopted to update the algorithm iteratively to boost the convergence performance. To verify the comprehensive performance of the IFNS-MOAPO algorithm, seven benchmark test problems are selected for simulation experiments and compared with five algorithms in terms of runtime duration, Pareto front plots, and metric values. The results show that the IFNS-MOAPO algorithm has good distribution and can converge to the true Pareto front quickly. It is a useful tool for solving constrained multi-objective optimization problems (CMOPs).

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“…In this paper, we consider artificial physics optimization algorithms (see e.g. [19] - [21]) for solution of SP and SPP.…”

Section: Introductionmentioning

confidence: 99%

Problems of sensor placement for intelligent environments of robotic testbeds

Sheka¹

2013

ijma

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In this paper, we consider problems of sensor placement for intelligent environments of robotic testbeds. In particular, we consider the problem of sensor placement and the problem of sensor placement for triangulation based localization. We present experimental results for these problems. Also, we use artificial physics optimization algorithms for solution of the problem of sensor placement. We consider artificial physics optimization for different virtual forces. In particular, we use Runge Kutta neural networks for the calculation of values of virtual forces.

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The force law design of artificial physics optimization for starting population selection for GSAT

Abstract: GSAT is a well-known satisfiability search algorithm which uses some random functions. In this paper, we consider an artificial physics optimization for computing a function of starting population.

Cited by 10 publications

References 6 publications

A system of intelligent algorithms for a module of onboard equipment of mobile vehicles

A system of intelligent algorithms for a module of onboard equipment of mobile vehicles

An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion

Problems of sensor placement for intelligent environments of robotic testbeds

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