正弦餘弦演算法（SCA）是基於使用正弦和餘弦函數更新粒子群的集體搜尋演算法，與其他方法相比，SCA被證明具有更好的性能。儘管SCA的性能已經比其他方法好，但它趨於過早收斂。當SCA用於解決真實案例研究時，它會面臨停滯局部最佳解、收斂速度慢和跳過全域最佳解的問題。因此，有必要對原始演算法進行改善，使其在解決某些優化問題時可以獲得更好的表現。
就像過去文獻一樣，本研究也將改善原始SCA演算法的表現。根據過去文獻顯示，慣性權重的實現可提高集體搜尋演算法的表現，是因為它可以平衡廣度與深度搜尋演算法，從而避免過早收斂。對於停滯局部最小問題，可以應用的另一變異方法。應用慣性權重和突變可提供原始SCA更好的表現。
本論文採用三種不同的案例驗證所提出的方法，包括無限制連續函數、限制連續設計問題和離散優化問題。為了解提出方法的有效性，將比較經典的萬用演算法包括遺傳演算法和粒子群最佳化演算法。實驗結果證實，與原始演算法和其他經典演算法相比，所提出的演算法在大多數連續最佳化問題中都能獲得較好的表現，而對於離散問題，遺傳演算法則能得到較好的解。

Sine-cosine algorithm (SCA) considered as population based algorithm and uses two equations, which are sine and cosine functions, to update particle position. SCA proved to have better performance compared to other approaches. Even though it already performs better than other approaches, SCA tends to converge prematurely. SCA also faces local optima stagnation, slow convergence, and skipping the true solutions when it is used to solve real cases. Thus, there is a need to improve the original algorithm so it can get the better performance when it is used to solve some optimization problems.
Several research have already been performed to improve the SCA. Like previous study, this research also tries to increase the performance of the original SCA. Based on the literature review that has been carried out, the implementation of the inertia weight provides a promising result to increase the performance of population-based algorithms because it can balance the search algorithm between exploration and exploitation. Therefore, the premature convergence can be avoided. For local minima stagnation problem, another technique that can be applied is mutation. The applications of inertia weight and mutation are expected to provide better performance of original SCA.
The proposed method are tested on 3 different kinds of case studies including unconstrained continuous functions, constrained continuous design problems, and discrete optimization problems. The proposed method is also compared to other classical metaheuristics namely genetic algorithm and particle swarm optimization algorithm to see its effectiveness. The experimental result shows that the proposed variant shows its superiority in most of continuous optimization problems compared to its original algorithm and other classical algorithms, while for discrete problems, it is outperformed by GA.

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