A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

Abstract

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.

Modified Atom Search Optimization Based on Immunologic Mechanism and Reinforcement Learning

“…Determine the parameters. (2) While the stop criterion is not satis ed do (3) For each atom X i do (4) Calculate the massing using equations (2) and 3; 5Determine its K neighbors using equation 4; (6) Calculate the interaction force F i and the constraint force G i using equations (11) and 12, respectively; 7Calculate the acceleration using equation 14; (8) Update the velocity using equation 15; (9) Update the position using equation 16; (10) Check out, update the tness values; (11) Invoke the reinforcement learning operator by equations (20) and 21; (12) Invoke the vaccination operator by equations (17) and 18; (13) Invoke the immune detection operator by equation 19; (14) End For. (15) End While (16) Find the best solution so far X Best ALGORITHM 2: Pseudocode of the modi ed atom search optimization based on the immunologic mechanism and reinforcement learning.…”

“…End If. (9) Calculate the massing using equations (2) and 3; (10) Determine its K neighbors using equation 4; (11) Calculate the interaction force F i and the constraint force G i using equations (11) and (12), respectively; (12) Calculate the acceleration using equation 14; (13) Update the velocity using equation 15; (14) Update the position using equation 16; (15) End For. (16) End While.…”

“…Compared with traditional optimization methods, the swarm intelligence algorithm has simple principle and fewer parameters and does not need gradient information of the problem. At present, the common swarm intelligence algorithms include particle swarm optimization algorithm [4], differential evolution algorithm [5], cuckoo search algorithm [6], flower pollination algorithm [7], sine-cosine search algorithm [8][9][10], and artificial bee colony algorithm [11].…”

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

“…In view of the shortcomings of the SCA algorithm, scholars at home and abroad have made some improvements. Some scholars introduced search strategies of other algorithms or improved the setting of the parameters of the SCA algorithm itself: Long et al [49] introduced nonlinear weight factor and inertial weight based on Gaussian distribution, respectively, to improve the ability to avoid falling into the trap of local optimization and convergence speed; Qu et al [50] proposed an improved SCA algorithm based on neighborhood search and Greedy Levy Mutation to better balance the phases of local exploitation and global exploration of the algorithm. Some scholars have also integrated the SCA algorithm with other algorithms to further improve the optimization ability of the algorithm: Chegini et al [51] proposed to mix the SCA algorithm with the PSO algorithm to improve the global search capability of the algorithm and the calculation accuracy; Nenavath and Jatoth [52] mixed the SCA algorithm and the DE algorithm to further improve the convergence speed of the algorithm and the ability to avoid local optimization.…”

A modified sine cosine algorithm for improving wind plant energy production

“…In (5), the symbols α and γ are the positive constant values that are introduced to regulate the portion of exploration and exploitation during the tuning process. Although there are several researchers that proposed exponential [23] and quadratic [24] curves in the original r1, their are limited to only one curve, while (5) can generate two curves during the whole iterations. As a result, it is expected that our new 1 r can provide more choices of exploration and exploitation portions as compared to the exponential and quadratic versions.…”