Abstract
Increasing complexity of real world problems motivated an area to explore efficient optimization methods to solve such problems. Existing optimization algorithms cannot solve all type of problems efficiently (NFL theorem), so new algorithms are proposed to find the better solutions for such complex optimization problems. However, their efficiency and performance can be still improved. Therefore, to follow this vital purpose, in this paper, a novel metaheuristic algorithm, called intelligent clonal optimizer (ICO), is proposed to solve continuous optimization problems. In the proposed algorithm, the initial population is generated through the chaos theory to enhance its exploration capability. It lacks any crossover operator. Instead, a novel clonal operator copying candidate solutions according to their fitness in a self-adaptive way is proposed. Cloning each parent is carried out by two methods, and according to these methods, each offspring is located near the parent or in direction of temporary target. The offsprings are classified to two classes. In addition, a novel conservative selection operator is proposed. According to this operator, the new population is selected from two classes of offsprings and current population by maintaining population diversity. The performance of the ICO algorithm is assessed on 39 well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC2019 benchmark functions as well as three engineering application problems. Results of the proposed ICO are compared to sixteen state-of-art metaheuristic algorithms in three categories including the most well-known and recently developed algorithms and the best performer of IEEE CEC competitions using statistical analysis, scalability analysis, Wilcoxon Signed-Rank Test, Friedman test, computational time analysis and convergence analysis. The obtained results proved that ICO performs better than state-of-art metaheuristics in sense of scalability and accurate convergence. According to average rank of Friedman test, the proposed ICO is firstly ranked among others.
Published Version
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