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The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. In this paper we apply InterCriteria analysis (ICrA) on hybrid Ant Colony Optimization (ACO) algorithm for Multiple Knapsack Problem (MKP). The aim is to study the hybrid algorithm behavior comparing with traditional ACO algorithm. Based on the obtained numerical results and on the ICrA approach the efficiency and effectiveness of the proposed hybrid ACO, combined with appropriate local search procedure are confirmed.

In this paper, the definitions of four operations over 3-dimensional index matrices are discussed. Some of their properties are discussed and an examples for their use are given.

Optimization of the production process is important for every factory or organization. The better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm to solve the problem.We investigate the algorithm performance according evaporation parameter. The aim is to find the best parameter setting.

Every organization and factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimize the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure.

The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker must performs a minimum number of hours of any assigned job. There is a maximum number of jobs that can be assigned and a maximum number of workers that can be assigned. There is a set of jobs that shows the jobs on which the worker is qualified. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. On this work we propose a variant of Ant Colony Optimization (ACO) algorithm to solve workforce optimization problem. The algorithm is tested on a set of 20 test problems. Achieved solutions are compared with other methods, as scatter search and genetic algorithm. Obtained results show that ACO algorithm performs better than other two algorithms.

Osteoporosis is a growing major public health problem with impact that crosses medical, social, and economic lines. It is now recognized that it is extremely important to diagnose osteoporosis before a fragility fracture occurs. The purpose of the present study is to give an example of application of Generalized Nets in orthopedics and traumatology and to propose a novel approach for diagnosing the asymptomatic osteoporosis in elderly patients.

In this investigation different algorithms for InterCriteria relations calculation are proposed. The algorithms are investigated by exploring the influence of genetic parameters on algorithm performance during the model parameter identification of E. coli fermentation process. Four different algorithms performing InterCriteria Analysis (ICrA), namely μ-biased, balanced, ν-biased and unbiased, are applied. Proposed ICrA algorithms are compared based on real experimental data set of an E. coli MC4110 fed-batch fermentation process. The obtained results show that for considered here case study the most reliable algorithm is the μ-biased one.

In this paper the apparatus of generalized nets is applied to describe the Cuckoo search (CS). The CS is a metaheuristic population-based algorithm inspired by the brood parasitism of some cuckoo species laying their eggs in the nests of other bird species. In addition, CS is enhanced by the so-called Levy flights rather than by simple isotropic random walks. Preliminary studies show that CS is very promising and could outperform existing algorithms such as Particle Swarm Optimization, Genetic Algorithms, and other algorithms. The proposed herewith model provides the opportunity to describe the logic of CS in the terms of the mathematical modeling paradigm of generalized nets.

In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimization problems obtaining good results. In this paper we propose dynamic parameter adaptation of the BA using Interval Type-2 fuzzy logic. Simulation results show that the proposed method using Type-2 fuzzy logic is better in comparison with Type-1 fuzzy logic.

In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets - namely InterCriteria Analysis. The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process. Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value. Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.

In this paper, a hybrid scheme, to solve optimization problems, using a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) is introduced. In the hybrid GA-ACO approach, the GA is used to find a feasible solutions to the considered optimization problem. Next, the ACO exploits the information gathered by the GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by the GA. To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to the parameter identification problem in the E. coli MC4110 fed-batch fermentation process model. Moreover, a comparison with both the conventional GA and the stand-alone ACO is presented. The results show that the hybrid GA-ACO takes the advantages of both the GA and the ACO, thus enhancing the overall search ability and computational efficiency of the solution method.

In this paper a Generalized Net (GN) model of the regulation of the lac operon in E. coli bacterium is presented. Prokaryotes, such the bacterium E. coli, have an efficient mechanism for metabolizing lactose. Three proteins that are important in lactose metabolism are all encoded in a single expressible unit of DNA, called the lac operon. The GN model, presented in this paper, describes the use of lactose as an energy source from E. coli. The net presents the turn on of several genes in the lac operon, which are required for lactose metabolism.

The apparatus of Generalized Nets (GN) is applied here to describe different kinds of genetic algorithms (GA). Failure of conventional optimization methods to lead to a satisfied solution in parameter identification of non-linear and time-dependent parameters provokes an idea some stochastic algorithms to be applied. As such genetic algorithms (GA), as a promising metaheuristic technique, are widely used. Different modifications of simple genetic algorithms (SGA) have been investigated and successfully applied to parameter identification of fermentation processes aiming to improve the model accuracy and the algorithm convergence time. Altogether six modifications of SGA have been proposed with a different sequence of implementation of basic genetic operators selection, crossover and mutation. In the present GN model the user is allowed to choose the sequence of execution of main GA operators, thus resulting in one of the six considered here modifications of SGA. © 2012 IEEE.

The apparatus of Generalized Nets is applied here to a description of a selection operator, which is one of the basic genetic algorithm operators. This genetic operator performs a probabilistic selection based upon the individual's fittness such that the better individuals have an increased chance of being selected for the next generation. The resulting GN model could be considered as a separate module, but can also be accumulated into a GN model to describe a whole genetic algorithm.