Journal of Algorithms and Computation
Volume:55 Issue: 1, Jun 2023

Let $G$ be a graph. Let $f:V\left(G\right)\rightarrow \left\{0,1,2,\ldots,k-1\right\}$ be a function where $k\in \mathbb{N}$ and $k>1$. For each edge $uv$, assign the label $f\left(uv\right)=\left\lceil \frac{f\left(u\right)+f\left(v\right)}{2}\right\rceil$. $f$ is called a $k$-total mean cordial labeling of $G$ if $\left|t_{mf}\left(i\right)-t_{mf}\left(j\right) \right| \leq 1$, for all $i,j\in\left\{0,1,2,\ldots,k-1\right\}$, where $t_{mf}\left(x\right)$ denotes the total number of vertices and edges labelled with $x$, $x\in\left\{0,1,2,\ldots,k-1\right\}$. A graph with admit a $k$-total mean cordial labeling is called $k$-total mean cordial graph. In this paper we investigate the $4$-total mean cordial labeling behaviour of some spider graph.

This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism SearchAlgorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex andpractical engineering optimization problems. The obtained results confirm the high performance of the proposedalgorithm in comparison to the other well-known algorithms.

As science and technology is progressing in engineering problems are also getting much more complex. So, solving these problems is of pivotal concern. Besides, the optimal solution among the solutions is of great value. Among them, innovative algorithms inspired by artificial intelligence or the hunting behavior of animals in nature have a special place. In this article, a new algorithm named Giant Trevally Optimizer (GTO) is presented, by simulating the hunting strategy of this type of fish, a novel algorithm with the same title is introduced, which has been examined, and subjected to various tests and criteria. In the performance studies of the GTO algorithm with several efficient meta-heuristic algorithms to find the global optimal solution, fifteen criterion functions having various features along with two hard problems in engineering design were used. The performance of the GTO algorithm has been better than other algorithms.

A random walk is a special kind of stochastic process of the Markov chain type. Some stochastic processes can be represented as a random walk on a graph. In this paper, the main parameters for a random walk on graph are examined.

In this paper we investigate the pair difference cordial labeling behavior of double alternate triangular snake and double alternate quadrilatral snake graphs.

Predicting missing links in noisy protein-protein interaction networks is an essential~computational method. Recently, attributed network embedding methods have been shown to be significantly effective in generating low-dimensional representations of nodes to predict links; in these representations, both the nodes'features and the network's topological information are preserved. Recent research suggests that models based on paths of length 3 between two nodes are more accurate than models based on paths of length 2 for predicting missing links in a protein-protein interaction network. In the present study, an attributed network embedding method termed ANE-SITI is recommended to combine protein sequence information and network topological information. In addition, to improve accuracy, network topological information also considers paths of length 3 between two proteins. The results of this experiment demonstrate that ANE-SITI outperforms the compared methods on various~protein-protein interaction (PPI) networks.

The topic of feature selection has become one of the hottest subjects in machine learning over the last few years. The results of evolutionary algorithm selection have also been promising, along with standard feature selection algorithms. For K-Nearest Neighbor (KNN) classification, this paper presents a hybrid filter-wrapper algorithm based on Equilibrium Optimization (EO). With respect to the selected feature subset, the filter model is based on a composite measure of feature relevance and redundancy. The wrapper model consists of a binary Equilibrium Optimization (BEO). The hybrid algorithm is called filter-based BEO (FBBEO). By combining filters and wrappers, FBBEO achieves a unique combination of efficiency and accuracy. In the experiment, 11 standard datasets from the UCI repository were utilized. Results indicate that the proposed method is effective in improving the classification accuracy and selecting the best optimal features subsets with the least number of features.

The Kalman-Bucy filter is studied under different scenarios for observation and state equations, however, an important question is, how this filter may be applied to detect the change points. In this paper, using the Bayesian approach, a modified version of this filter is studied which has good and justifiable properties and is applied in change point analysis.

This paper is about producing a new kind of pairs which we call MS-pairs. To produce these pairs, we use an algorithm for dividing a natural number $x$ by two for two arbitrary numbers and consider their related graphs. We present some applications of these pairs that show their interesting properties such as unpredictability, irreversible, aperiodicity and chaotic behavior.

The imperialist competitive algorithm (ICA) is developed based on the socio-political process of imperialist competitions. It is an efficient approach for single-objective optimization problems. However, this algorithm fails to optimize multi-objective problems (MPOs) with conflicting objectives. This paper presents a modification of the ICA to different multi-objective problems. To improve the algorithm performance and adapt to the characteristics of MOPs, the Sigma method was used to establish the initial empires, the weighted sum approach (WSum) was employed for empire competition, and an adaptive elimination approach was used for external archiving strategy. the results indicated that the suggested algorithm had a higher performance compared to other algorithms based on diversity and convergence characteristics.