Meta-heuristic algorithms play an essential role in solving real-world optimizationproblems. However, their performance is limited by the complexity and variability of the problems. Hence, various efficient algorithm...
详细信息
Meta-heuristic algorithms play an essential role in solving real-world optimizationproblems. However, their performance is limited by the complexity and variability of the problems. Hence, various efficient algorithms are being actively explored. The exponential distribution optimizer (EDO), having attracted attention for its efficient search performance, has been extended to several applications. However, it suffers from falling into local optima and weak exploitation. Meanwhile, it cannot be directly applied to solve binary optimizationproblems. To address these challenges, this paper proposes an enhanced EDO called BOMLDEDO. The Bernstein-assisted oppositional-multiple learning strategy is proposed to avoid falling into local optimality. The Bernstein-based adaptive differential strategy is developed to improve exploitation capability. Moreover, by introducing a transfer function, repair method, and binary-to-real operation, BOMLDEDO is extended to a binary version. The IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) test functions and engineering problems are used to evaluate BOMLDEDO's optimization performance for continuousproblems. Compared to its competitors, BOMLDEDO ranks first on more than 8 out of 10 IEEE CEC 2020 functions and more than 10 out of 12 IEEE CEC 2022 functions. Meanwhile, it achieves the global optimum in 91% of engineering problems. Furthermore, the 0-1 knapsack problems are applied to verify BOMLDEDO's binary optimization capabilities, and the results show that BOMLDEDO is successfully utilized in 14 knapsack instances. The above results demonstrate that incorporating multiple strategies helps improve the performance of BOMLDEDO, making it more reliable and applicable in solving continuous optimization problems and 0-1 knapsack problems.
This paper presents a dynamic multi-swarm particle swarm optimization based on an elite learning strategy (DMS-PSO-EL). In DMS-PSO-EL, the whole evolutionary process is divided into a former stage and a later stage. T...
详细信息
This paper presents a dynamic multi-swarm particle swarm optimization based on an elite learning strategy (DMS-PSO-EL). In DMS-PSO-EL, the whole evolutionary process is divided into a former stage and a later stage. The former and later stages are focus on the exploration and the exploitation, respectively. In the former stage, the entire population is divided into multiple dynamic sub-swarms and a following sub-swarm according to the particles' fitness values. In each generation, the dynamic sub-swarms evolve independently, which is beneficial for keeping population diversity, while particles in the following sub-swarm choose elites in the dynamic sub-swarms as their learning exemplars aiming to find out more promising solutions. To take full advantages of the different sub-swarms and then speed up the convergence, a randomly dynamic regrouping schedule is conducted on the entire population in each regrouping period. In the latter stage, all the particles select the historical best solution of the entire population as an exemplar aiming to enhance the exploitation ability. The comparison results among DMS-PSO-EL and other 9 well-known algorithms on CEC2013 and CEC2017 test suites suggest that DMS-PSO-EL demonstrates superior performance for solving different types of functions. Furthermore, the sensitivity and performance of the proposed strategies in DMS-PSO-EL are also testified by a set of experiments.
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 theo...
详细信息
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 optimizationproblems. 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. Ac
Tree-Seed Algorithm is a kind of swarm intelligence optimization algorithm. It is used to solve various problems widely, but it still has some shortcomings need to be overcame, such as imbalance between exploration an...
详细信息
Tree-Seed Algorithm is a kind of swarm intelligence optimization algorithm. It is used to solve various problems widely, but it still has some shortcomings need to be overcame, such as imbalance between exploration and exploitation, local stagnation, premature convergence, and so on. In this study, an enhanced meta-heuristic optimization algorithm, called Migration Tree-Seed Algorithm (MTSA), is proposed inspiring by Grey Wolf Optimizer (GWO). The hierarchical gravity learning and random-based migration mechanisms are introduced to overcome the intrinsic defects of the basic TSA. Firstly, hierarchy mechanism ensures the tree migration to guide the seed generation avoiding the local stagnation. Secondly, random-based migration mechanism increases the seed diversity to improve the exploration ability. Finally, the coordinated update of the two mechanisms actuate a suitable trade-off between exploration ans exploitation. We use IEEE CEC 2014 benchmark functions to compare MTSA with basic TSA, the TSA variants (STSA, EST-TSA, fb_TSA), GWO, ABC, SCA, BOA, JAYA and RSA. MTSA is subsequently applied to three classical engineering design problems reported in the specialized literature. Both results show that the MTSA is very competitive and effective compared with other well-known meta-heuristics, proving its excellent applicability in real-world challenging problems with unknown search spaces.
暂无评论