Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

Hu Hanyu,Shan Weifeng,Tang Yixiang,Heidari Ali Asghar,Chen Huiling,Liu Haijun,Wang Maofa,Escorcia-Gutierrez José,Mansour Romany F,Chen Jun 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6

The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.

A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

Su Hang,Zhao Dong,Yu Fanhua,Heidari Ali Asghar,Xu Zhangze,Alotaibi Fahd S,Mafarja Majdi,Chen Huiling 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1

As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.

Addressing constrained engineering problems and feature selection with a time-based leadership salp-based algorithm with competitive learning

Qaraad Mohammed,Amjad Souad,Hussein Nazar K,Elhosseini Mostafa A 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6

Like most metaheuristic algorithms, salp swarm algorithm (SSA) suffers from slow convergence and stagnation in the local optima. The study develops a novel Time-Based Leadership Salp-Based Competitive Learning (TBLSBCL) to address the SSA’s flaws. The TBLSBCL presents a novel search technique to address population diversity, an imbalance between exploitation and exploration, and the SSA algorithm’s premature convergence. Hybridization consists of two stages: First, a time-varying dynamic structure represents the SSA hierarchy of leaders and followers. This approach increases the number of leaders while decreasing the number of salp’s followers linearly. Utilizing the effective exploitation of the SSA, the position of the population’s leader is updated. Second, the competitive learning strategy is used to update the status of the followers by teaching them from the leaders. The goal of adjusting the salp swarm optimizer algorithm is to help the basic approach avoid premature convergence and quickly steer the search to the most promising likely search space. The proposed TBLSBCL method is tested using the CEC 2017 benchmark, feature selection problems for 19 datasets (including three high-dimensional datasets). The TBLSBCL was then evaluated using a benchmark set of seven well-known constrained design challenges in diverse engineering fields defined in the benchmark set of real-world problems presented at the CEC 2020 conference (CEC 2020). In each experiment, TBLSBCL is compared with seven other state-of-the-art metaheuristics and other advanced algorithms that include seven variants of the salp swarm. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. According to the experimental data and statistical tests, the TBLSBCL algorithm is very competitive and often superior to the algorithms employed in the studies. The implementation code of the proposed algorithm is available at: https://github.com/MohammedQaraad/TBLSBCL-Optimizer.

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

Yazdani, Maziar,Jolai, Fariborz Society for Computational Design and Engineering 2016 Journal of computational design and engineering Vol.3 No.1

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

최적화 기법 효율성 개선을 위한 Multi-layered Harmony Search Algorithm의 개발 및 적용

이호민(Lee, Ho Min),유도근(Yoo, Do Geun),이의훈(Lee, Eui Hoon),최영환(Choi, Young Hwan),김중훈(Kim, Joong-Hoon) 한국산학기술학회 2016 한국산학기술학회논문지 Vol.17 No.4

최적화 분야에서 Harmony Search Algorithm (HSA)은 근래에 개발된 메타휴리스틱 최적화 알고리즘의 하나로, HSA 가 개발된 이래 공학, 자연과학, 의약학 등 다양한 분야에서 많은 연구자들에 의해 활용되어왔다. 현재 최적화 대상 문제들의 복잡성이 날로 증가하고 있으며 이에 따라 기존 최적화 기법에 대한 효율을 개선하는 방법론 개발에 대한 필요성이 대두되고 있다. 따라서 본 연구에서는 HSA의 구조적 특성에 초점을 맞추어 해탐색 능력을 향상시키는 것을 목표로 하여 새로운 메타 휴리스틱 최적화 알고리즘인 Multi-layered Harmony Search Algorithm (MLHSA)을 제안하였다. 개발된 MLHSA는 기존 HSA 에 추가적으로 구조적인 특성을 부여하여 전역 탐색 및 지역 탐색 성능을 개선하였다. 또한, 제안된 기법의 효율성과 적용성 을 검증하기 위해 이전 최적화 알고리즘 관련 문헌에서 다양한 알고리즘이 적용된 바 있는 수학적 최적해 탐색 문제와 상수 도 관망의 최적 관경 설계에 MLHSA를 통한 최적화를 수행하였다. 적용 결과 본 연구에서 개발된 MLHSA는 기존 알고리즘 을 효과적으로 대체할 수 있는 최적화 기법임을 확인할 수 있었다. The Harmony Search Algorithm (HSA) is one of the recently developed metaheuristic optimization algorithms. Since the development of HSA, it has been applied by many researchers from various fields. The increasing complexity of problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms are required. In this study, to improve the HSA in terms of a structural setting, a new HSA that has structural characteristics, called the Multi-layered Harmony Search Algorithm (MLHSA) was proposed. In this new method, the structural characteristics were added to HSA to improve the exploration and exploitation capability. In addition, the MLHSA was applied to optimization problems, including unconstrained benchmark functions and water distribution system pipe diameter design problems to verify the efficiency and applicability of the proposed algorithm. The results revealed the strength of MLHSA and its competitiveness.

Scheduling Jobs on Cloud Computing using Firefly Algorithm

Demyana Izzat Esa,Adil Yousif 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.7

Cloud computing is a new technology, instead of all computer hardware and software that used on desktop, or somewhere within company's network, it's presented as a service by cloud service providers and accessed via the Internet. Exactly where hardware and software are located and how everything works does not matter. In cloud computing there are many jobs that requires to be executed on the available resources to achieve best minimal execution time. Several optimization methods are available for cloud job scheduling. However, the job scheduling process is still need to be optimized. This paper proposes a new job scheduling mechanism using Firefly Algorithm to minimize the execution time of jobs. The proposed mechanism based on information of jobs and resources such as length of job speed of resource and identifiers. The scheduling function in the proposed job scheduling mechanism firstly creates a set of jobs and resources to generate the population by assigning the jobs to resources randomly and evaluates the population using a fitness value which represents the execution time of jobs. Secondly the function used iterations to regenerate populations based on firefly behavior to produce the best job schedule that gives the minimum execution time of jobs. Several scenarios are implemented using Java Language and CloudSim simulator. Different settings have been considered in the evaluation and experimentation phase to examine the proposed mechanism in different workloads. The first phase of the evaluation process describes how the proposed mechanism can be used to minimize the execution time of jobs. The second phase of the evaluation process compares the proposed mechanism with First Come First Serves (FCFS) algorithm. The results revealed that the proposed mechanism minimizes the execution time significantly. Furthermore, the proposed mechanism outperformed the FCFS algorithm.

매개변수 자가적응 화음탐색 알고리즘의 성능 비교를 통한 최적해 탐색 효율 향상

최영환(Choi Young Hwan),이호민(Lee Ho Min),유도근(Yoo Do Guen),김중훈(Kim Joong Hoon) 한국산학기술학회 2018 한국산학기술학회논문지 Vol.19 No.1

다양한 공학분야의 최적화 문제를 해결하기 위해 적절한 매개변수를 설정하기란 번거로운 작업이며, 매개변수 민감도 분석을 통해 적절한 매개변수를 설정하더라도 설정된 매개변수가 모든 문제에 적절한지 판단하기에는 한계가 있다. 이러한 이유로 매개변수를 문제에 따라 적절하게 설정하는 매개변수 자동검보정 (Self-adaptive) 화음탐색 알고리즘이 개발되고 발전하고 있다. 본 연구에서는 지금까지 개발된 자가적응형 하모니서치를 조사하고 그의 특성을 해탐색, 설정 매개변수, 적용성 등으로 구분하였으며, 이 중 매개변수 설정의 번거로움을 없애고, 적절한 매개변수 설정을 통해 해의 성능 향상을 위해 개발 된 6 가지 자가적응형 화음탐색 알고리즘을 선택하여 비교 분석을 수행하였다. 최적화 결과의 객관적인 비교를 위해 대표적인 수학적, 공학적 최적화 문제를 모두 적용 하였고, 다양한 성능 지수 (Performance index)를 사용하여 각 알고리즘의 성능을 정량적으로 비교하였다. 이것은 향후 신규 최적화 알고리즘을 개발하거나 해 탐색의 성능을 향상시키는 연구에 도움이 될 것으로 기대된다. In various engineering fields, determining the appropriate parameter set is a cumbersome and difficult task when solving optimization problems. Despite the appropriate parameter setting through parameter sensitivity analysis, there are limits to evaluating whether the parameters are appropriate for all optimization problems. For this reason, kinds of a Self-adaptive Harmony searches have been developed to solve various engineering problems by the appropriate setting of algorithm`s own parameters according to the problem. In this study, various types of Self-adaptive Harmony searches were investigated and the characteristics of optimization were categorized. Six algorithms with a differentiation of optimization process were applied and compared with not only the mathematical optimization problem, but also the engineering problem, which has been applied widely in the algorithm performance comparisons. The performance of each algorithm was compared, and the statistical performance indicators were used to evaluate the application results quantitatively.

An Comparative Study of Metaheuristic Algorithms for the Optimum Design of Structures

Yeon-Sun RYU(류연선),Hyun-Man CHO(조현만) 한국수산해양교육학회 2017 水産海洋敎育硏究 Vol.29 No.2

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.

A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations

Abdelmonem M. Ibrahim,Mohamed A. Tawhid 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.3

In this study, we propose a new hybrid algorithm consisting of two meta-heuristic algorithms; Differential Evolution (DE) and the Monarch Butterfly Optimization (MBO). This hybrid is called DEMBO. Both of the meta-heuristic algorithms are typically used to solve nonlinear systems and uncon-strained optimization problems. DE is a common metaheuristic algorithm that searches large areas of candidate space. Unfortunately, it often requires more significant numbers of function evaluations to get the optimal solution. As for MBO, it is known for its time-consuming fitness functions, but it traps at the local minima. In order to overcome all of these disadvantages, we combine the DE with MBO and propose DEMBO which can obtain the optimal solutions for the majority of nonlinear systems as well as unconstrained optimization problems. We apply our proposed algorithm, DEMBO, on nine different, unconstrained optimization problems and eight well-known nonlinear systems. Our results, when com-pared with other existing algorithms in the literature, demonstrate that DEMBO gives the best results for the majority of the nonlinear systems and unconstrained optimization problems. As such, the experimen-tal results demonstrate the efficiency of our hybrid algorithm in comparison to the known algorithms.

A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems

Yıldız Betül Sultan,Mehta Pranav,Panagant Natee,Mirjalili Seyedali,Yildiz Ali Riza 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6

This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.