Discrete Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

This paper proposes a binary adaptation of the recently proposed meta-heuristic, Equilibrium Optimizer (EO), called Discrete EO (DEO) to solve binary optimization problems. A U-shaped transfer function has been used to map the continuous values of EO into the binary domain. To further improve the exploitation capability of DEO, Simulated Annealing (SA) has been used as a local search procedure and the combination has been named as DEOSA. The proposed DEOSA algorithm has been applied over 18 well-known UCI datasets and compared with a wide range of algorithms. The results have been statistically validated using Wilcoxon rank-sum test. In order to test the scalability and robustness of DEOSA, it has been additionally tested over 7 high-dimensional Microarray datasets and 25 binary Knapsack problems. The results clearly demonstrate the superiority and merits of DEOSA when solving binary optimization problems.

A Hybrid Grasp-genetic Algorithm for Mixed-model Assembly Line Balancing Problem Type 2

In manufacturing systems, mixed model assembly lines are used to produce different products to deal with the problem of customers’ demands variety, and minimizing the cycle time in such assembly line is a critical problem. This paper addresses the mixed model assembly line balancing problem type 2 that consists in finding the optimal cycle time for a given number of workstations. A hybrid Greedy randomized adaptive search procedure-Genetic algorithm is proposed to find the optimal assignment of tasks among workstations that minimize the cycle. A Ranked Positional Weight heuristic is used in the construction phase of the proposed GRASP, and in the local search phase, a neighborhood search procedure is used to ameliorate the constructed solutions in the construction phase. The GRASP is executed many times in order to seed the initial population of the proposed genetic algorithm, and the results of the executions are compared with the final solutions obtained by the hybrid GRASP-GA. In order to test the proposed approaches, a numerical example is used.

Administrative proceedings in Roman law

The article deals with the issues of administrative proceedings in Roman law. Its beginning was laid in the 5th century BC by the laws of ХII tables. They briefly deal with the issues of legal proceedings, which at that time was called court agreement. Free citizens gathered at the forum (city square), where the plaintiff presented claims to the defendant. In Roman law, the rule was established to make claims through claims, which the magistrate (praetor) had to support. The judges were elected by the people. Gradually, the praetors changed the formulas of claims, which contributed to the development of Roman law. Often they proceeded not from the law, but from the circumstances of the case, therefore they could instruct the judge to consider the case “in good faith.” Gradually, various forms of claims were formed, which contributed to the improvement of administrative proceedings. In Roman law, many concepts were laid that have remained in modern legal proceedings. This is the election and turnover of judges, the necessary defense, the writ of execution and the persons who monitored their execution; jurisdiction; corruption; search procedure; jurisdiction; privilege; appeal against court decisions; requirement for witnesses; consideration of cases by the police; torts; limitation of actions; and some others. This makes the study of Roman justice relevant.

On the Poisson Stability to Study a Fourth-Order Dynamical System with Quadratic Nonlinearities

This article discusses the search procedure for Poincaré recurrences to classify solutions on an attractor of a fourth-order nonlinear dynamical system, using a previously developed high-precision numerical method. For the resulting limiting solution, the Lyapunov exponents are calculated, using the modified Benettin’s algorithm to study the stability of the found regime and confirm the type of attractor.

Finding Optimal Algorithm in Artificial Intelligence

Present days many artificial intelligence search algorithms are plays a important to figure out the problem of shortest path finding. The paper presents the detailed study of heuristic search and blind search techniques. The paper focus additional in the direction of blind search strategies such as Breadth First Search, Depth First Search, and Uniform Cost Search and informed explore strategies like A*, and Best First Search. The paper consist of effective of search procedure, their qualities, and demerits, where these algorithms are applicable, also at last comparison of search techniques based on complexity, optimality and completeness are presented in tabular structure.

Artificial Bee Colony Algorithm based on a New Local Search Approach for Parameter Estimation of Photovoltaic Systems

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and PV modules of PV systems for parameter estimations. The success of the ABC-Ls method was compared with the algorithms commonly used in the literature such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm and standard ABC algorithm, and the comparison results were presented in tables and graphics in detail. It was found that the ABC-Ls method was more successful and achieved better results compared to GA, PSO and standard ABC. It was determined that the ABC-LS method performed similar or better PV parameter estimation compared to other studies in the literature.

Multi-Objective GRASP for Maximizing Diversity

This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same time must be obtained. A novel adaptation of the well-known greedy randomized adaptive search procedure, which has been traditionally used for single-objective optimization, was proposed. Two new constructive procedures are presented to generate a set of efficient solutions. Then, the improvement phase of the proposed algorithm consists of a new efficient local search procedure based on an exchange neighborhood structure that follows a first improvement approach. An effective exploration of the exchange neighborhood structure is also presented, to firstly explore the most promising ones. This feature allowed the local search proposed to limit the size of the neighborhood explored, resulting in an efficient exploration of the solution space. The computational experiments showed the merit of the proposed algorithm, when comparing the obtained results with the best previous method in the literature. Additionally, new multi-objective evolutionary algorithms derived from the state-of-the-art were also included in the comparison, to prove the quality of the proposal. Furthermore, the differences found were supported by non-parametric statistical tests.