Population Of Initial Solutions Research Articles

Seasons optimization algorithm

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the forest is referred to as a tree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees’ life cycles in nature: renew, competition, seeding, and resistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

Bayesian networks (BNs) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. However, learning BN structure from data is a typical NP-hard problem. In this paper, we present a novel hybrid algorithm for BN structure learning, called MMABC. It’s based on a recently introduced meta-heuristic, which has been successfully applied to solve a variety of optimization problems: Artificial Bee Colony (ABC). MMABC algorithm consists of three phases: (i) obtain an initial undirected graph by the subroutine MMPC. (ii) Generate the initial population of solutions based on the undirected graph and (iii) perform the ABC algorithm to orient the edges. We describe all the elements necessary to tackle our learning problem, and experimentally compare the performance of our algorithm with two state-of-the-art algorithms reported in the literature. Computational results demonstrate that our algorithm achieves better performance than other two related algorithms.

Non-dominated sorting differential evolution algorithm for the minimization of route based fuel consumption multiobjective vehicle routing problems

In this paper, three parallel multi-start non-dominated sorting differential evolution algorithms (PMS-NSDEs) are proposed for the solution of four multiobjective route based fuel consumption vehicle routing problems (MRFCVRPs) and their results are compared with the results of a parallel multi-start NSGA II algorithm. All these algorithms use more than one initial population of solutions. In each algorithm a variable neighborhood search algorithm for the improvement of each solution separately is used. The problems that are formulated with two competitive objective functions are the multiobjective symmetric and asymmetric delivery route based fuel consumption vehicle routing problem (MSDRFCVRP and MADRFCVRP) and the multiobjective symmetric and asymmetric pick-up route based fuel consumption vehicle routing problem (MSPRFCVRP and MAPRFCVRP). The objective functions correspond to the optimization of the time needed for the vehicle to travel between two customers or between the customer and the depot and to the route based fuel consumption of the vehicle considering the traveled distance, the load of the vehicle, the slope of the road, the speed and the direction of the wind, and the driver’s behavior when the decision maker plans delivery or pick-up routes. A number of modified Vehicle Routing Problem instances are used in order to measure the quality of the proposed algorithms.

System Application of Genetic Algorithm for Scheduling Optimization Study Using Java (Case Study : Department of Computer System UNTAN)

cheduling of lecture in a university is a college activities into the work place and time that there is room in such a way so as to minimize the violation of the scheduling problems of a lecture. Problems that are often referred to as the University timetabling problem ( UTP ) is, requires a lot of consideration include the number of students, number of teachers who are not proportional to the number of courses, the amount of space used, as well as lecture predetermined time. Scheduling lectures with the automation system is very important because it can save you hours of work and provide optimum solutions in a short time, which can increase productivity, the quality of teaching and learning, and quality of service. One method that can be used to complete the course scheduling problem is a genetic algorithm approach. Genetic algorithm is an optimization tool that models the principles of evolution. Genetic algorithm is able to find a globally optimum solution in the search space is very complex. By using an initial population of solutions is encoded and selected based on its quality, and then used to create a new population using crossbreeding and mutation process of the initial individuals. Evaluation function is used to calculate the hard constraints and soft constraints can be met. This study discusses the scheduling college located in Department of Computer Systems Untan. The purpose of study is to optimize the college scheduling and to make easier the scheduling job with make a college scheduling software

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems

This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

A hybrid evolutionary algorithm for the job shop scheduling problem

In this paper, a hybrid metaheuristic method for the job shop scheduling problem is proposed. The optimization criterion is the minimization of makespan and the solution method consists of three components: a Differential Evolution-based algorithm to generate a population of initial solutions, a Variable Neighbourhood Search method and a Genetic Algorithm to improve the population; the latter two are interconnected. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high quality solutions in short computational times using fixed parameter settings.

USING HYBRID GENETIC ALGORITHMS TO SOLVE DISCRETE LOCATION ALLOCATION PROBLEMS WITH RECTILINEAR DISTANCE

This paper presents a hybrid genetic algorithm to solve the uncapacitated location allocation problems as a combinatorial optimization problem. The proposed method incorporates a modified K-means algorithm that clusters the customers into groups based on the rectilinear distance, and then the initial population of solutions is calculated according to the derived centers of clusters. The hybrid genetic algorithm consists of two evolutionary processes synchronized with each other. It uses the elite strategy, local search, multi-start mechanism and distance-based mutation to efficiently obtain the optimal or approximated solutions. In order to verify the effectiveness of the proposed method, four GAs-KGA, HGA, LMGA and TGA, are built and compared. Furthermore, using six commonly used test problems, the performance of the proposed method is evaluated relative to the Simulated Annealing method [4] and the SOFMGR method [14] as benchmarks. Experimental results show that the proposed method is excellent in quality of solutions and speed of computation.

USING HYBRID GENETIC ALGORITHMS TO SOLVE DISCRETE LOCATION ALLOCATION PROBLEMS WITH RECTILINEAR DISTANCE

ABSTRACT This paper presents a hybrid genetic algorithm to solve the uncapacitated location allocation problems as a combinatorial optimization problem. The proposed method incorporates a modified K-means algorithm that clusters the customers into groups based on the rectilinear distance, and then the initial population of solutions is calculated according to the derived centers of clusters. The hybrid genetic algorithm consists of two evolutionary processes synchronized with each other. It uses the elite strategy, local search, multi-start mechanism and distance-based mutation to efficiently obtain the optimal or approximated solutions. In order to verify the effectiveness of the proposed method, four GAs—KGA, HGA, LMGA and TGA, are built and compared. Furthermore, using six commonly used test problems, the performance of the proposed method is evaluated relative to the Simulated Annealing method [4] and the SOFMGR method [14] as benchmarks. Experimental results show that the proposed method is excellent in quality of solutions and speed of computation.

Genetic Algorithms in Analytical Chemistry

ABSTRACT Genetic algorithms (GA's) are search algorithms that imitate nature with their Darwinian survival of the fittest approach. They are well suited for searching among a large number of possibilities for solutions because they exploit knowledge contained in a population of initial solutions to generate new and potentially better solutions. GA's have several advantages over conventional search techniques. First, GA's consider many points in the search space simultaneously. Because GA's utilize parallelism in which a large number of candidate solutions are simultaneously searched, more of the response surface is probed, so there is a reduced chance of convergence to a local minimum. Second, genetic algorithms make no assumption about the geometry of the response surface. Hence, discontinuities or singularities in the response surface, which rule out the use of derivative or simplex based methods, will not pose a problem for GA's. Third, the computational environment offered by a GA can be readily adjusted to match a particular application. Thus, GA's can be tailored for individual problems. Consequently, GA's can be used to solve a variety of data analysis problems in chemistry including curve fitting, parameter estimation, function optimization, calibration, classification, and wavelength and feature selection.