A novel application of crossover operator to a hybrid optimization framework: Investigation into cutting problems

Abstract

The Generate and Solve (GS) is a hybrid optimization framework that combines a metaheuristic engine (genetic algorithm), which works as a generator of reduced instances of the original optimization problem, and an integer programming solver. GS has been recently introduced in the literature and achieved promising results in cutting and packing problem instances. In this paper, we present a novel application of crossover operator, the Uniform Order-Based Crossover, to the GS framework. As a means to assess the potentialities behind the novel application, we provide as instantiation of the framework for dealing specifically with the constrained two-dimensional non-guillotine cutting problem. Computational experiments performed over standard benchmark problems are reported and discussed here, evidencing the effectiveness of the novel operator.

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

“…Another promising line of investigation involves the design and implementation of parallel/distributed versions of the framework, by means of which several GRI (Generator of reduced instances) and SRI (solver of reduced instances) instances could run concurrently, each one configured to explore different aspects of the optimization problem at hand. The use of insular genetic algorithms [25][26][27][28] can bring more diversity and possibilities, resulting in effectiveness enhancement. Moreover, other metaheuristics such as particle swarm could be experimented replacing or working cooperatively with genetic algorithms.…”

“…The use of insular genetic algorithms [25][26][27][28] can bring more diversity and possibilities, resulting in effectiveness enhancement. Moreover, other metaheuristics such as particle swarm could be experimented replacing or working cooperatively with genetic algorithms.…”

Tackling the Irregular Strip Packing problem by hybridizing genetic algorithm and bottom-left heuristic

“…The tests were executed on a desktop machine with an Intel i5 processor running at 3.60 GHz and with 4 GB of RAM. For the sake of computational performance, our Genetic Algorithm was configured with roulette wheel selection; uniform order-based crossover, which was introduced by Davis [31] and whose application has recently achieved promising results in a particular C&P problem [32]; and swap mutation. After preliminary experiments, we set the parameters discussed in Section III-A as follows: n = 400 individuals, τ c = 0.9 and τ m = 0.2.…”