Milica Šelmić

Bee colony optimization for the p-center problem

Bee colony optimization (BCO) is a relatively new meta-heuristic designed to deal with hard combinatorial optimization problems. It is biologically inspired method that explores collective intelligence applied by the honey bees during nectar collecting process. In this paper we apply BCO to the p-center problem in the case of symmetric distance matrix. On the contrary to the constructive variant of the BCO algorithm used in recent literature, we propose variant of BCO based on the improvement concept (BCOi). The BCOi has not been significantly used in the relevant BCO literature so far. In this paper it is proved that BCOi can be a very useful concept for solving difficult combinatorial problems. The numerical experiments performed on well-known benchmark problems show that the BCOi is competitive with other methods and it can generate high-quality solutions within negligible CPU times.

Bee colony optimization for scheduling independent tasks to identical processors

The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.

Combining case-based reasoning with Bee Colony Optimization for dose planning in well differentiated thyroid cancer treatment

Highlights? We used Case-Based Reasoning to describe a physicians expertise when treating thyroid cancer. ? We took into account various clinical parameters. ? The weights of these parameters are determined with the Bee Colony Optimization meta-heuristic. ? The proposed CBR-BCO model suggests the I-131 iodine dose in radioactive iodine therapy. ? This approach is tested on real data from the Department of Nuclear Medicine, Serbia. Thyroid cancers are the most common endocrine carcinomas. Case-based reasoning (CBR) is used in this paper to describe a physicians expertise, intuition and experience when treating patients with well differentiated thyroid cancer. Various clinical parameters (the patients diagnosis, the patients age, the tumor size, the existence of metastases in the lymph nodes and the existence of distant metastases) influence a physicians decision-making in dose planning. The weights (importance) of these parameters are determined here with the Bee Colony Optimization (BCO) meta-heuristic. The proposed CBR-BCO model suggests the I-131 iodine dose in radioactive iodine therapy. This approach is tested on real data from patients treated in the Department of Nuclear Medicine, Clinical Center Kragujevac, Serbia. By comparing the results that are obtained through the developed CBR-BCO model with those resulting from the physicians decision, it has been found that the developed model is highly reflective of reality.

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.

Scheduling independent tasks: Bee Colony Optimization approach

The problem of static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. The problem is solved by the Bee Colony Optimization (BCO). The BCO algorithm belongs to the class of stochastic swarm optimization methods. The proposed algorithm is inspired by the foraging habits of bees in the nature. The BCO algorithm was able to obtain the optimal value of objective function in all small to medium size test problems. The CPU times required to find the best solutions by the BCO are acceptable.

Bee Colony Optimization Approach to Solving the Anticovering Location Problem

Bee colony optimization (BCO) is a relatively new metaheuristic designed to deal with hard combinatorial optimization problems. It belongs to the group of nature-inspired methods that explore collective intelligence applied by the honey bees during the nectar collecting process. In this paper, BCO is applied to the anticovering location problem (ACLP), one of the fundamental problems in the area of discrete location. Because a BCO algorithm has not been used in the literature related to the ACLP so far, it was a challenge to test its performances on this nondeterministic polynomial time (NP)-hard problem. The numerical experiments performed on the well known benchmark problems show that the proposed algorithm can generate high-quality solutions in reasonable CPU times.

The bee colony optimization algorithm and its convergence

The bee colony optimization BCO algorithm is a nature-inspired meta-heuristic method for dealing with hard, real-life combinatorial and continuous optimisation problems. It is based on the foraging habits of honeybees and was proposed by Lucic and Teodorovic in 2001. BCO is a simple, but effective meta-heuristic method that has already been successfully applied to various combinatorial optimisation problems in transport, location analysis, scheduling and some other fields. This paper provides theoretical verification of the BCO algorithm by proving some convergence properties. As a result, the gap between successful practice and missing theory is reduced.

Determining the number of postal units in the network - Fuzzy approach, Serbia case study

One of the main, current, goals of the public postal operators in developing countries is to define the model for proper postal network design and access. The access to the postal network represents a set of different elements that interact with each other and have a common aim of providing continuous, of high quality, reliable and sustainable universal postal service. Worldwide experience suggests different approaches in defining the components and criteria for establishing the system of access to the postal network of the public operator. In this paper we present two different approaches. The first one is based on criteria determined in previous study and, here, we develop proper mathematical formulation. The second one is new, general, method created to generate fuzzy rules from numerical data, well known as Wang-Mendels method. The authors apply both methods on real data collected from Serbian municipalities and finally, compare results obtained by them.

Parallelization strategies for bee colony optimization based on message passing communication protocol

The Bee Colony Optimization (BCO) algorithm is a meta-heuristic that belongs to the class of biologically inspired stochastic swarm optimization methods, based on the foraging habits of bees in nature. BCO operates on a population of solutions, and therefore, it represents a good basis for parallelization. The main contribution of this work is the development of new and efficient parallelization strategies for BCO. We propose two synchronous and two asynchronous parallelization strategies for a distributed memory multiprocessor architecture under the Message Passing Interface (MPI) communication protocol. The first synchronous strategy involves independent execution of several BCO algorithms, while the second one implements cooperation between these algorithms. The asynchronous strategies are implemented in two ways: with centralized and non-centralized communication controls. The presented experimental results, addressing the problem of static scheduling independent tasks on identical machines, show that our parallel BCO algorithms provide excellent performance. As for the case of independent execution, a significant speedup is obtained while preserving the solution quality. Compared to the sequential execution, cooperative strategy leads to better quality solutions within the same amount of wall-clock time, as long as it is applied to a modest number of processors engaged in parallel BCO execution. As this number increases, asynchronous strategies outperform the other ones with respect to both solution quality and running time.

Ride Matching Using K -means Method: Case Study of Gazela Bridge in Belgrade, Serbia

Transport networks in many cities are generally very seriously congested. Consequently, travel time, number of stops, unexpected delays, transport costs, level of air pollution, noise, and traffic accidents are increased. In addition to daily congestion, there may be congestion and traffic jams as a consequence of reconstruction of the roads’ lanes. During the past decade, different strategies for transport demand management have been developed, with the aim to decrease existing traffic congestion. One of the available strategies is a ride matching (sharing) concept. This strategy means that several participants share only one private car when traveling from origin to destination. A model for grouping drivers into cars for ride matching, according to similarities of a place of living and working, working hours, and type of car license plate, is developed in this paper. The writers apply the ride matching concept on congested traffic during the reconstruction of the main bridge in Belgrade, Serbia, which i...