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.

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 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.

Minimization of the discrimination time between the backup and main overcurrent relay is one of the most critical issue in relay coordination of power system. Determination of time setting multipliers (TSMs) using evolutionary Algorithms has been studied in previous papers. In this paper, TSM, various characteristics of the overcurrent relays and pickup currents are simultaneously considered to improve coordination of main and backup overcurrent relays. Furthermore, the coordination problem can also be considered as an optimization problem which can be solved using artificial intelligent methods. Recently, a novel optimization Algorithm, called Hyper-Sphere Search (HSS) Algorithm, has been introduced. In this paper, Improved HSS (IHSS) is introduced. Based on the problems mentioned in this paper, the IHSS Algorithm is more appropriate for obtaining the characteristics of the overcurrent relays, pickup currents and their TSMs. The result of IHSS is compared with HSS Algorithm which has been used in previous studies. The simulation results on the test network show the efficiency of using IHSS and considering pickup currents in term of better relays coordination.

The combined heat and power (CHP) generation is an energy-saving method in which power and heat are simultaneously generated. The important features of heat and power units are the interdependence between the amount of power and heat generated, which increases the complexity of the problem and can be solved by powerful optimization methods. In this paper, planning and optimizing the combined heat and power generation, including the supply of electrical and thermal power required by the network, are performed. This goal is achieved with two main approaches, including the valve point effect and examining this effect while modeling load peak in the presence of energy storage systems of the battery type, which is the novelty of this paper. This paper offers the Hyper-spherical Search (HSS) Algorithm and particle swarm optimization method to optimize and solve the problem of CHPED in large units and, then, compares and validates the results. The simulation results show that the proposed methods can be used as a reliable method to solve the problem of CHPED by considering different constraints.

Random based inventive Algorithms are being widely used for optimization. An important category of these Algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed Algorithm for optimizing the orientation game is a series of optimization Algorithms that are formed with the idea of an old game and the Search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the Algorithm is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic Algorithms.

In this paper, a new Sphere decoding Algorithm is introduced which is a modification of the Viterbo-Boutros (VB) Algorithm. In the proposed Algorithm, the problem of the initial selection of the Search Sphere radius in the VB Algorithm has been removed, and furthermore, the speed of the Algorithm is increased. To examine the performance of the proposed Algorithm, we employ it in detection of signals in code division multiple access (CDMA) and multiple-input multiple-output (MIMO) systems. Computer simulations, by using the proposed Algorithm, show a significant performance improvement with respect to other suboptimal detection methods.