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.

Public transport plays an important role in our live.It is very important to have a reliable service.Up to 1000 km, trains and buses play the main role in the public transport.The number of the people and which kind of transport they prefer is important information for transport operators.In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method.The problem is described as multi-objective optimization problem.There are two optimization purposes: minimal transportation time and minimal price.Some fuzzy element is included.When the price is in a predefined interval it is considered the same.Similar for the starting traveling time.The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time.

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.

In combinatorial optimization, the goal is to find the optimal object from a finite set.Since such problems are hard to be solved, usually some metaheuristics is applied.One of the most successful techniques for a number of classes of problems is Ant Colony Optimization (ACO).Some start strategies can be applied, to the ACO algorithms, to improve their performance.Here, the InterCriteria Analysis (ICrA) is applied to the ACO algorithm.On the basis of the ICrA, we examine and analyse the ACO performance according to the different start strategies.

In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters -crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes.The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand.The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.

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 fedbatch 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.