A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems

Dadvar, Marziyeh; Navidi, Hamidreza; Javadi, Hamid Haj Seyyed; Mirzarezaee, Mitra

doi:10.1007/s10489-021-02605-x

Cited by 22 publications

(13 citation statements)

References 30 publications

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“…Meanwhile, the purpose of the main function is to call all artificial intelligence algorithms regarding weights, variables, and dimensions of an engineering system with auto-tuning methods, such as in an intelligent thing. The novel algorithm will search for a more suitable algorithm to be performed in a considered number of iterations and parameters so that reasonably precise results can be achieved in a fast-running time [26] [27].…”

Section: Nb Theorymentioning

confidence: 99%

NB Theory with Bargaining Problem: A New Theory

Mohamadwasel

Ma’arif

2022

IJRCS

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A new theory is proposed to achieve desired set of values for an engineering system based on a single algorithm that consists of all optimization algorithms. This algorithm considers an engineering system's main variables, dimensions, and functions; all these parameters will be set and tuned by a systematic search in the main library. This single algorithm automatically selects an optimization algorithm suitable for a particular optimization problem. Additionally, this theory, which was inspired by the idea of bargaining theory and Noorulden Basil theory, referred to as NB Theory, can be done using MATLAB Software.

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Section: Nb Theorymentioning

confidence: 99%

NB Theory with Bargaining Problem: A New Theory

Mohamadwasel

Ma’arif

2022

IJRCS

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“…This method has no requirements on the mechanical configuration of the robot, and the problem of singularity will not occur. In the HBC algorithm, each individual represents a set of joint angles, and the current end-effector posture matrix can be calculated by the forward kinematics equation, as shown in equation (22).…”

Section: Kinematics and Objective Functionmentioning

confidence: 99%

“…The second is to combine an algorithm with other algorithms. Dadvar et al 22 proposed a new hybrid algorithm (HGPSODE). Comparing with the classic algorithm and other hybrid models, the HGPSODE algorithm has better performance.…”

Section: Introductionmentioning

confidence: 99%

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

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To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.

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“…Based on ( 21), the standard HPSO algorithm draws lessons from genetic algorithm about crossover operation and mutation operation when updating particles [36].…”

Section: ) Standard Hpsomentioning

confidence: 99%

High-Fidelity Decision-Making and Simulation for Cooperative Autonomous Air Combat Considering the Effect of Flight Controller

Wang

Wang²,

et al. 2022

IEEE Access

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In the past decade, how improving the fidelity of maneuver gaming and the synergy of target allocation has become key issues in cooperative autonomous air combat researches. To address these two problems, this study proposes a maneuver decision-making algorithm based on an optimized dynamic Bayesian network and a target allocation decision-making algorithm based on an optimized hybrid particle swarm optimization. In maneuvering decision-making, the state transition's reliability and the air combat's autonomy are enhanced through considering the effect of sliding mode control. The Bayesian network is improved through introducing a strategy for dynamic prior probability updating. The computation is reduced and the efficiency is increased through pruning the minimax search tree according to visual prediction. In target allocation decision-making, the algorithm's convergence speed is greatly accelerated and the solution's global optimality is improved through introducing immigrant particles. The algorithm's application scope is expanded through proposing a solution principle about unequal quantity combat situations. Furthermore, the end criterion is specially designed to fit real-world combats through introducing a fire control. The simulation results show that the designed decision-making algorithms are more effective in solving the problem of cooperative autonomous air combat, which shows that the various improvements introduced in this study are reasonable and effective.INDEX TERMS Cooperative autonomous air combat, maneuver decision-making, target allocation decision-making, sliding mode controller, dynamic Bayesian network, hybrid particle swarm optimization.

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A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems

Cited by 22 publications

References 30 publications

NB Theory with Bargaining Problem: A New Theory

NB Theory with Bargaining Problem: A New Theory

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

High-Fidelity Decision-Making and Simulation for Cooperative Autonomous Air Combat Considering the Effect of Flight Controller

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