Solving 0-1 knapsack problem by continuous ACO algorithm

This paper presents a continuous ACO approach to solve 0-1 knapsack problem. In this method, groups of candidate values of the components are constructed, and an amount of pheromone is initialised randomly for each candidate value a real random number between 0.1 and 0.9 in each candidate group. To solve binary knapsack problem for each object a candidate group is constructed where candidate value is either 0 or 1. Each ant selects a value from each group to construct a path or a solution. After certain number of generation, store the best solution in a temporary population. When temporary population size is equal to the number of ants, then temporary population will be considered as initial population by re-initialising fresh set of pheromone. This procedure will continue until the maximum generation defined is reached. In experimental section, we compare the results of standard test functions and 0-1 knapsack problem with existing literature.

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