Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local Search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local Search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

A Hyper Spherical Search (HSS) optimization algorithm based on chaos theory is proposed that resolves the weakness of the standard HSS optimization algorithm including the speed of convergence and the sequential increment in the number of algorithm iterations to achieve the optimal solution. For this, in the particle initiation and Search steps of the proposed algorithm, random values used in the standard algorithm are replaced with the values of two mappings, Chebyshev and Liebovitch, that makes the results of the proposed algorithm definite and decreases their standard deviation. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate Search algorithm to find the optimal solution in comparison to standard hyper spherical Search algorithm and some other optimization algorithms such as genetic, particle swarm, and harmony Search algorithm.

A hybrid optimization algorithm based on genetic algorithm and choatic hyper spherical Search method is proposed. In the proposed method, in order to increase the efficiency of Searching the optimal solution, chaos theory along with genetic operators have been used. This, not only makes the results of the proposed algorithm definite and decreases their standard deviation, but also resolves the weakness of the hyper spherical Search optimization algorithm based on chaos theory including the speed of convergence and the weak performance in some benchmark functions. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate Search algorithm to find the optimal solution in comparison to the standard hyper spherical Search algorithm, chaotic hyper sherical Search algorithm, and some other optimization algorithms such as genetic, particle swarm, and harmony Search algorithm.

This article introduces two swarm intelligent algorithms, a group Search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group Search-artificial fish swarm algorithm (GS-AFSA). This algorithm has been applied to three different discrete truss optimization problems. The optimization results are compared with those obtained using the standard GSO, the AFSA and the quick group Search optimizer (QGSO). The proposed GS-AFSA eliminated the shortcomings of GSO regarding falling into the local optimum by taking advantage of AFSA’s stable convergence characteristics and achieving a better convergence rate and convergence accuracy than the GSO and the AFSA. Furthermore, the GS-AFSA has a superior convergence accuracy compared to the QGSO, all while solving a complicated structural optimization problem containing numerous design variables.

This paper presents a strategy for using Harmony Search algorithm in facility layout optimization problems. In this paper an adapted harmony Search algorithm is developed for solving facility layout optimization problems. This method finds an optimal facility arrangement in an existing layout. Two real-world case studies are employed to demonstrate the efficiency of this model. A comparison is also made to illustrate the efficiency of these strategies in facility layout optimization.