Abstract
To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.
Highlights
With the development of big data, cloud computing, artificial intelligence, and other technologies, the data size of networks has witnessed fast growth, and as a result, it has been more common to solve optimization problems that are similar to traffic networks, such as vehicle route planning, spacecraft design, and wireless sensor layouts [1]
Unlike meta heuristics, which are independent of the problem, heuristic algorithms depend on a specific problem
Since the algorithm we proposed has two loops, in order to show fairness when compared with other algorithms, we set Tmax to 10 and Tmax to 100, so the maximum number of iterations is
Summary
With the development of big data, cloud computing, artificial intelligence, and other technologies, the data size of networks has witnessed fast growth, and as a result, it has been more common to solve optimization problems that are similar to traffic networks, such as vehicle route planning, spacecraft design, and wireless sensor layouts [1]. As to the problems with the HS algorithm in solving high-dimensional multi-objective optimization problems, Zhang proposed an improved differential evolved harmony search algorithm. In this algorithm, mutation and crossover are adopted to substitute the original pitch adjustment in the HS optimization algorithm, improving the global search ability of the algorithm [21]. To solve the main problems with the algorithm, such as its poor global search ability and poor solution accuracy, a reward population-based differential harmony search algorithm is proposed. In a numerical experiment, the validity of the algorithm is verified through comparison
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