DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

Yin, Shihong; Luo, Qifang; Du, Yanlian; Zhou, Yongquan

doi:10.3934/mbe.2022105

Cited by 38 publications

(8 citation statements)

References 105 publications

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“…To verify whether there is a significant difference between the solution results of EOSMA and each comparison algorithm, the Wilcoxon rank-sum test of two paired samples was utilized 84 . Figure 11 illustrates the p -value of the Wilcoxon rank-sum test as a bar graph.…”

Section: Resultsmentioning

confidence: 99%

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Yin

Luo

Zhou

et al. 2022

Sci Rep

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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

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Section: Resultsmentioning

confidence: 99%

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Yin

Luo

Zhou

et al. 2022

Sci Rep

Self Cite

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“…Therefore, methods such as self-adaptive improvement of fixed parameters or the introduction of self-adaptive weight factors to balance the development and exploration performance of SMA have gradually become effective measures for many researchers. For example, ASMA [ 24 ] is proposed by adopting a suitable mechanism for adaptively selecting SMA control parameters and introducing an adversarial learning operator; DTSMA [ 25 ] uses adaptive t-distribution variation balance to enhance the ability to explore and exploit; and AOSMA uses an adaptive approach to decide whether opposition-based learning (OBL) will be used or not [ 26 ]. Moreover, from the experimental results, it can be seen that the global optimization results have significantly improved with the adaptive optimization of the slime mould algorithm compared with the original algorithm.…”

Section: Related Workmentioning

confidence: 99%

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Zhang

Liu

Bai³

2023

PLoS ONE

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In this article, an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the capacitated vehicle routing problem (CVRP). This improvement is based on the mixed-strategy optimization of Cauchy mutation and simulated annealing to alleviate the lack of global optimization capability of the SMA. By introducing the Cauchy mutation strategy, the optimal solution is perturbed to increase the probability of escaping from the local extreme value; in addition, the annealing strategy is introduced, and the Metropolis sampling criterion is used as the acceptance criterion to expand the global search space to enhance the exploration phase to achieve optimal solutions. The performance of the proposed SMA-CSA algorithm is evaluated using the CEC 2013 benchmark functions and the capacitated vehicle routing problem. In all experiments, SMA-CSA is compared with ten other state-of-the-art metaheuristics. The results are also analyzed by Friedman and the Wilcoxon rank-sum test. The experimental results and statistical tests demonstrate that the SMA-CSA algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the capacitated vehicle routing problem demonstrate its efficiency and discrete solving ability.

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“…SMA has been applied to many applications, such as engineering problems [13], global optimization [14], wireless sensor networks [15], and optimal reactive power dispatch [16].…”

Section: Figure 1 the Graph G And Its Resolving Graph R(g)mentioning

confidence: 99%

Hybridizing Slime Mould Algorithm with Simulated Annealing for Solving Metric Dimension Problem<br /> <div> <br /> </div>

Mohamed,

Amin

2023

MLR

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In this paper, we consider the NP-hard problem of finding the metric dimension of graphs. A set of vertices B of a connected graph G = (V, E) resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. The cardinality of the smallest resolving set of G is the metric dimension of G. The metric dimension problem arises in several different fields, such as robot navigation, telecommunication, and geographical routing protocol. The slime mould algorithm (SMA) is an efficient population-based optimizer based on the oscillation mode of slime mould in nature. The SMA has a specific mathematical model and very competitive results, along with fast convergence for many problems, particularly in realworld cases. SMA has good exploration and exploitation abilities for solving optimization problems. However, complex and high-dimensional SMA may fall into local optimal regions. SA is a very preferable technique among the other heuristic approaches as it provides practical randomness in the search to avoid the local extreme points. However, SA involves a tradeoff between computing time and solution sensitivity. The SA is used to enhance the fitness of the best agent if it falls in a suboptimal region, which will lead to the enhancement of all individuals. We solve the problem as integer linear programming and introduce the hybrid algorithm SMA-SA, which combines simulated annealing SA and SMA for determining the metric dimension of graphs. Comparisons were performed on the graphs: k-home chain graph, tadpole graph, alternate triangular snake graph, and mirror graph. Finally, computational results and comparisons with pure SA, SMA, and PSO algorithms confirm the effectiveness of the proposed SMA-SA for solving metric dimension problem.

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DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

Cited by 38 publications

References 105 publications

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Hybridizing Slime Mould Algorithm with Simulated Annealing for Solving Metric Dimension Problem<br /> <div> <br /> </div>

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DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

Cited by 38 publications

References 105 publications

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Hybridizing Slime Mould Algorithm with Simulated Annealing for Solving Metric Dimension Problem&lt;br /&gt; &lt;div&gt; &lt;br /&gt; &lt;/div&gt;

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Hybridizing Slime Mould Algorithm with Simulated Annealing for Solving Metric Dimension Problem<br /> <div> <br /> </div>