Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem.

Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem; second, transferring knowledge.

Removal Capacity and Mechanism of Modified Chitosan for Ochratoxin A Based on Rapid Magnetic Separation Technology.

Ochratoxin A (OTA) exposure in food is very dangerous to human health. Therefore, the development of a fast and efficient technique for OTA removal has become an urgent research topic in the field of food safety. Nano FeO modified chitosan nanocomposite (nano-FeO@CTS) was synthesized as a rapidly separable and safe adsorbent and was used to adsorb OTA in wine.

A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for big data optimization.

Introduction: Big data optimization (Big-Opt) problems present unique challenges in effectively managing and optimizing the analytical properties inherent in large-scale datasets. The complexity and size of these problems render traditional data processing methods insufficient.

Objectives: In this study, we propose a new multi-objective optimization algorithm called the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA) to solve Big-Opt problem.

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one.

Optimal stochastic power flow using enhanced multi-objective mayfly algorithm.

For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case.

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy.

Advances in Slime Mould Algorithm: A Comprehensive Survey.

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm.

Functional extreme learning machine.

Introduction: Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. However, the ELM also has some shortcomings, such as structure selection, overfitting and low generalization performance.

Methods: This article a new functional neuron (FN) model is proposed, we takes functional neurons as the basic unit, and uses functional equation solving theory to guide the modeling process of FELM, a new functional extreme learning machine (FELM) model theory is proposed.

Multi-objective pathfinder algorithm for multi-objective optimal power flow problem with random renewable energy sources: wind, photovoltaic and tidal.

In this paper, the multi-objective optimal power flow (MOOPF) problem optimization objectives focus on four optimization objectives: generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications, including wind energy, solar energy, and tidal energy are introduced. Renewable energy supply is uncertain, so Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively.

Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses.

Introduction: The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.

Methods: In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.

Functional extreme learning machine for regression and classification.

Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines.

An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time Windows.

The vehicle routing problem with time windows (VRPTW) is a classical optimization problem. There have been many related studies in recent years. At present, many studies have generally analyzed this problem on the two-dimensional plane, and few studies have explored it on spherical surfaces.

Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis.

Clustering is an unsupervised learning technique widely used in the field of data mining and analysis. Clustering encompasses many specific methods, among which the K-means algorithm maintains the predominance of popularity with respect to its simplicity and efficiency. However, its efficiency is significantly influenced by the initial solution and it is susceptible to being stuck in a local optimum.

Parameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization.

The vehicle routing problem (VRP) problem is a classic NP-hard problem. Usually, the traditional optimization method cannot effectively solve the VRP problem. Metaheuristic optimization algorithms have been successfully applied to solve many complex engineering optimization problems.

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

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.

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree.

Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed.

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

The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed.

CWCA: Complex-valued encoding water cycle algorithm.

Since the meta-heuristic water cycle algorithm (WCA) was presented, it has been used extensively in scientific computation and engineering optimization. The aims of this study are to improve the exploration and exploitation capabilities of the WCA algorithm, accelerate its convergence speed, and enhance its calculation accuracy. In this paper, a novel complex-valued encoding WCA (CWCA) is proposed.

An efficient binary Gradient-based optimizer for feature selection.

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. Gradient-based optimizer (GBO) is a recently developed metaheuristic with population-based characteristics inspired by gradient-based Newton's method that uses two main operators: the gradient search rule (GSR), the local escape operator (LEO) and a set of vectors to explore the search space for solving continuous problems. This article presents a binary GBO (BGBO) algorithm and for feature selecting problems.

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.

Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs.

[Identification of hexavalent chromium reducing bacteria Cr4-1 and optimization of its reduction conditions].

Objective: To identify the hexavalent chromium reduction Cr4-1, and to study the better conditions of the bacterial growth and its Cr(VI) reduction.

Methods: The physiological and biochemical methods and 16s rDNA sequencing were used for identification of bacteria Cr4-1. The influence of temperature, pH, initial Cr (VI) concentration and shaking speed on bacterial growth and Cr (VI) reduction were studied.

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases.

A hybrid monkey search algorithm for clustering analysis.

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution.

[Acclimating, screening of dominant bacteria for degrading pentachlorophenol and its degradation condition study].

Objective: To acclimate, screen the dominant bacteria for degrading pentachlorophenol and its growth characteristics and degradation conditions study.

Methods: Active sludge aeration and method of density gradient were used to acclimate the sludge. Using pentachlorophenol as a sole carbon source, it was increased step by step in continuous eight weeks.