A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Nenavath, Hathiram; Jatoth, Ravi Kumar; Das, Swagatam

doi:10.1016/j.swevo.2018.02.011

Cited by 142 publications

(46 citation statements)

References 54 publications

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“…To check the efficiency of the proposed algorithm, the modified fuzzy-controlled COBRA algorithm is tested on three different sets of test problems, which are 23 classical problems [50], nine standard benchmark problems with 10 and 30 variables [50], and 16 problems taken from the CEC 2014 competition [51]. These functions have been widely used in the literature [49] or [52], for example.…”

Section: Numerical Benchmarksmentioning

confidence: 99%

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

et al. 2020

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In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated.

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Section: Numerical Benchmarksmentioning

confidence: 99%

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

et al. 2020

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“…First, the proposed EBS-SCA first is compared with seven peer SCA algorithms: the original SCA [25], CGSCO [34], OBSCA [36], ISCA [38], LSCA [40], SCADE [42], and SCAPSO [43] on two sets of popular benchmark suites with 30-D problems. Actually, in the literature, the above seven SCA algorithms have rendered the promising performance and their parameter settings are listed in Table 1.…”

Section: ) Compared Various Metaheuristic Algorithmsmentioning

confidence: 99%

“…Nenavath and Jatoth [42] introduced a differential evolution technique as a local search operator to avoid premature convergence. Furthermore, Nenavath et al [43] proposed a hybrid SCA-PSO algorithm, which uses the Pbest and Gbest component of PSO as an internal historical memory to guide the entire search process and improve the premature convergence.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

Luo

Song

et al. 2019

IEEE Access

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The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability. INDEX TERMS Sine cosine algorithm (SCA), brain storm optimization (BSO), metaheuristic algorithm, global optimization.

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“…The standard SCA method starts from the random determination of a set of solutions in the search space. Then, the oscillating mathematical model based on disturbance rate is used to dynamically update the positions of all the individuals, and then the search regions can be explored with different sine and cosine return values [41][42][43]. During the evolutionary process, the greedy selection strategy is used to guarantee that the population can move in the better evolutionary direction, the evolution formula for the individuals in SCA is given as below: where r 1 is the variable used to balance the local exploration and global exploitation of the swarm.…”

Section: Sine Cosine Algorithm (Sca)mentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

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In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

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A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Cited by 142 publications

References 54 publications

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

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