5 research outputs found
A new evolutionary algorithm: Learner performance based behavior algorithm
A novel evolutionary algorithm called learner performance based behavior
algorithm (LPB) is proposed in this article. The basic inspiration of LPB
originates from the process of accepting graduated learners from high school in
different departments at university. In addition, the changes those learners
should do in their studying behaviors to improve their study level at
university. The most important stages of optimization; exploitation and
exploration are outlined by designing the process of accepting graduated
learners from high school to university and the procedure of improving the
learner's studying behavior at university to improve the level of their study.
To show the accuracy of the proposed algorithm, it is evaluated against a
number of test functions, such as traditional benchmark functions, CEC-C06 2019
test functions, and a real-world case study problem. The results of the
proposed algorithm are then compared to the DA, GA, and PSO. The proposed
algorithm produced superior results in most of the cases and comparative in
some others. It is proved that the algorithm has a great ability to deal with
the large optimization problems comparing to the DA, GA, and PSO. The overall
results proved the ability of LPB in improving the initial population and
converging towards the global optima. Moreover, the results of the proposed
work are proved statistically.Comment: 17 pages. Egyptian Informatics Journal, 202
Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
Agents of any metaheuristic algorithms are moving in two modes, namely
exploration and exploitation. Obtaining robust results in any algorithm is
strongly dependent on how to balance between these two modes. Whale
optimization algorithm as a robust and well recognized metaheuristic algorithm
in the literature, has proposed a novel scheme to achieve this balance. It has
also shown superior results on a wide range of applications. Moreover, in the
previous chapter, an equitable and fair performance evaluation of the algorithm
was provided. However, to this point, only comparison of the final results is
considered, which does not explain how these results are obtained. Therefore,
this chapter attempts to empirically analyze the WOA algorithm in terms of the
local and global search capabilities i.e. the ratio of exploration and
exploitation phases. To achieve this objective, the dimension-wise diversity
measurement is employed, which, at various stages of the optimization process,
statistically evaluates the population's convergence and diversity.Comment: 11 page