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shortcoming of these optimization techniques is the uncertainty as to the robustness of the algorithms
to locate the global minimum factor of safety rather than the local minimum factor of safety for
complicated and nonhomogeneous geological subsoil conditions (Goh, 1999). For this reason, Goh
(1999) has presented a genetic-based evolution technique to search for the critical slip surface of slope.
In this paper, an adaptive ant colony system is proposed to determine critical slip surface. It is used to
assess the influence of earthquake on the stability of slopes in earthquake zone. Ant colony system,
which is developed by imitating the colony behavior of ant colony, is mostly used to solve
combination optimization problems at present. For solving the complicated slope-stability problem, its
structure and the transfer probability of ant are ingeniously modified. Two examples are presented to
verify the effectiveness of the proposed approach.
ADAPTIVE ANT COLONY SYSTEM FOR SLOPE STABILITY ANALYSIS
Recently, ant colony system (ACS; Dorigo et al, 1996) is often applied to solve the combination
optimization problems, especially the traveling salesman problem (TSP; Dorigo and Gambardella,
1997). For ACS being fit for solving the problem of slope stability, the ant colony system's structure
and the computation method of the transfer probability of ant must be modified. For this purpose, a
slope is discrete by the separating lines and nodes shown in Figure 1 (Chen and Xie, 2002). An ant
starts from the point START, and then passes the nodes on the separating line one by one up to the
point END. Thus, a circulation is finished and a slip surface formed.
START ,
i
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V
A
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\
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\
-+1
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(r+
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END
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r
Figure 1 Search technique of critical slip surface of slope
It is noticed that the dotted lines in Figure 1 are only used to guide the ants to search for slip surfaces
and they are not considered in the computation of the slope safety factor.
Suppose that (r, /) represents the fth node on the rth separating line; (r+1, y) represents theyth node on
the (rfl)th separating line; [(r, i), (r+l,y)] represents the route from (r, i) to (r+1,;) and the number of
ants in ant colony is m. During the movement of ants, ant k (fc=l, 2, —, m) decides its transfer direction
according
to the pheromone quantity on the routes. At moment r, the probability P^r0(r+l;) (0 of ant k
transferring from (r, i) to (r+1,7) can be expressed as