Abstract
Some optimization problems need to finding a permutation of a given set of items that minimizes a certain cost function. This paper introduces an effective memetic algorithm for the linear ordering problem with cumulative costs (LOPCC). The proposed algorithm combines an order-based recombination operator with an improved forward-backward local search procedure and employs a quality based replacement criterion for pool updating. Extensive experiments on 118 benchmark instances from the literature show that the proposed algorithm achieves competitive results by identifying 46 new upper bounds. Furthermore, some critical ingredients of our algorithm are analyzed to understand the source of its performance.
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