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\iteman{io-port 06045801}
\itemau{Xie, Liping; Zeng, Jianchao; Formato, Richard A.}
\itemti{Convergence analysis and performance of the extended artificial physics optimization algorithm.}
\itemso{Appl. Math. Comput. 218, No. 8, 4000-4011 (2011).}
\itemab
Summary: This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual's best fitness history. Including the history improves EAPO's search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual's stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters $\{m_{i}, w, G\}$. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.
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\itemcc{}
\itemut{extended artificial physics optimization (EAPO); proof of convergence; artificial physics optimization (APO); physicomimetics; global optimization; gravitational force; virtual force; Newton's law}
\itemli{doi:10.1016/j.amc.2011.02.062}
\end