CHAC, A MOACO algorithm for computation of bi-criteria military unit path in the battlefield: Presentation and first results

Abstract

In this paper, we present a multiobjective ant colony optimization (MOACO) algorithm, called CHAC, designed to solve the problem of finding the path for a military unit that minimizes the cost in resources while maximizing safety. Unlike previous MOACO algorithms, CHAC uses a single colony and two different state transition rules: One that combines the heuristic and pheromone information of both objectives and another based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in different scenarios (maps with different degrees of difficulty), outperforming a greedy algorithm (taken as baseline), and yielding a good military behavior in the tactical sense. In comparison, the combined rule is slightly better than the rule based on dominance. C 2009 Wiley Periodicals, Inc.

AntBot: Ant Colonies for Video Games

“…is a parameter of the algorithm which controls the influence of these pheromones in the quality of the solutions. Rewriting (14) and (15) and setting up appropriate values for explorer ants gives the analogous equations (17) and otherwise (18) for which the quality of the solutions will be redefined as (19) Note that while (16) makes use of , the homologous equation (19) does not make use of . The reason for the above is that finding the nearest pill to every node is much more costly than computing the distance to the nearest ghost.…”

“…Such an approach can provide valuable information as, instead of a single solution, a set of solutions would be given from which new innovative design principles could be obtained [16]. Previous works on using multiobjective approach ant colonies to path finding can be found in [17], [18], and [19].…”

Influence of parameters on the performance of a MOACO algorithm for solving the bi-criteria military path-finding problem

“…τ0,s = 1 (nc · ME ) (15) with n c as the number of cells in the map to solve, M R as the maximum amount of resources going through a cell may require, and M E as the maximum cost in energy going through a cell may produce (in the worst case).…”

“…On the other hand, λ is a new parameter introduced in some bi-criteria ACOs (as hCHAC is) to set the importance of one objective over the other, so its influence is well known [15] (and chosen by the user).…”

“…hCHAC has been used to find the best path in several maps [15], [4], [5], obtaining the following values heuristically: α=1, β=2, ρ=0.1, and q 0 =0.4, which are their usual values except for q 0 , which usually takes the value 0.1; in our case, that makes the exploration component higher. The value for λ is usually chosen by the user, in order to set the priority of each one of the objectives.…”