Multi-objective Artificial Bee Colony Research Articles

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

The development of radio frequency identification (RFID) technology generates the most challenging RFID network planning (RNP) problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving multiobjective RNP (MORNP) has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC), the nondominated sorting genetic algorithm II (NSGA-II), and the multiobjective particle swarm optimization (MOPSO), on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

Abstract Construction site layout planning has been recognized as a critical step in construction planning. The basic function of this process is to find the best arrangement of the temporary facilities according to multiple objectives that may conflict with each other and subjected to logical and resource constraints. The formulation of the construction site layout planning problem as an optimization problem turns out to be a nonlinear programming problem where there are conflicting multi-objectives to be achieved. It is shown that the swarm intelligence based meta-heuristic algorithms are quite powerful in obtaining the solution of such hard to solve type of optimization problems. In this study a multi objective artificial bee colony (MOABC) via Levy flights algorithm is proposed to determine the optimum construction site layout. The model is intended to optimize the dynamic layout of unequal-area under two objective functions. The performance of MOABC with Levy flights is demonstrated on a real benchmark construction engineering of construction site layout planning problem and the optimum solution obtained is compared with the one determined by the ant colony algorithm.

Multi-objective artificial bee colony algorithm for short-term scheduling of hydrothermal system

In this paper, we present a multi-objective artificial bee colony (MOABC) algorithm and compare its efficiency with other existing algorithms for short-term scheduling of hydrothermal systems. We formulate the short-term combined economic and emission dispatch of hydrothermal systems as a complicated nonlinear optimization problem with a group of complex constraints. We modify the select operator of artificial bee colony algorithm to adapt the multi-objective problem optimization, and change the employed bee phase and probability calculation of onlooker bee phase to avoid local maxima. Furthermore, we utilize a progressive optimality algorithm based method to enhance the local search ability of the MOABC. Moreover, the constraint handling method has been proposed to resolve the complex constraints. We demonstrate the performance of the MOABC algorithm and compare it with other existing algorithms using the data from a hydrothermal power system in three different cases. The results show that the MOABC can obtain better schedule results with less fuel cost and environment pollution, more close to the true Pareto front and better diversification of non-dominated solutions compared to other existing methods.

Meta-schedulers for grid computing based on multi-objective swarm algorithms

Job scheduling is a challenging task on grid environments because they must fulfill user requirements. Scientists often have deadlines and budgets for their experiments (set of jobs). But these requirements are in conflict with each other – cheaper resources are slower than the expensive ones. In this paper, we have implemented two multi-objective swarm algorithms. One of them is based on a biological behavior – Multi-Objective Artificial Bee Colony (MOABC) – and the other on physics – Multi-Objective Gravitational Search Algorithm (MOGSA). Multi-objective properties enhance the optimization of execution time and cost per experiment. These algorithms are evaluated regard to the standard and well-known multi-objective algorithm – Non-dominated Sorting Genetic Algorithm II (NSGA II) – in order to prove the goodness of our multi-objective proposals. Moreover, they are compared with real meta-schedulers as the Workload Management System (WMS) from the most used European grid middleware, gLite, and the Deadline Budget Constraint (DBC) from Nimrod-G, that takes into account the same requirements. Results show us that MOABC offers better results in all the cases using diverse workflows with dependent jobs over different grid environments.

Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters.

Multi-objective artificial bee colony for interval job shop scheduling with flexible maintenance

In scheduling problem with uncertainty, flexible preventive maintenance (PM) and multiple objectives are seldom investigated. In this study, interval job shop scheduling problem with non-resumable jobs and flexible maintenance is considered and an effective multi-objective artificial bee colony (MOABC) is proposed, in which an effective decoding procedure is used to build the schedule and handle PM operation. The objective is to minimize interval makespan and a newly defined objective called total interval tardiness. In each cycle, a dominance-based greedy principle is adopted, a dominance-based tournament is utilized to choose solution for onlooker bee, and the non-dominated ranking is applied to update the non-dominated set. A solution with the highest rank is replaced with a non-dominated solution every certain cycle. Computational results show the good performance of MOABC on the considered problem.

A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm

In this paper, a novel multi-objective artificial bee colony algorithm is presented to solve the distribution system reconfiguration and hybrid (photo voltaic/wind turbine/fuel cell) energy system sizing. The purposes of the multi-objective optimization problem include the total power loss, the total electrical energy cost, and the total emission produced by hybrid energy system and the grid minimization, and the voltage stability index (VSI) of distribution system maximization. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated in each iteration. In addition, for preserving the diversity in the archive of Pareto solutions, the crowding distance operator is used. This algorithm is tested on 33 bus distribution systems and obtained non-dominated solutions are compared with the well-known NSGA-II and MOPSO methods. The solutions obtained by the MOABC algorithm have a good quality and a better diversity of the Pareto front compared with those of NSGA-II and MOPSO methods.

A new Multiobjective Artificial Bee Colony algorithm to solve a real-world frequency assignment problem

Artificial bee colony (ABC) is a recently introduced algorithm that models the behavior of honey bee swarm to address a multiobjective version for ABC, named Multiobjective Artificial Bee Colony algorithm (MO-ABC). We describe the methodology and results obtained when applying the new MO-ABC metaheuristic, which was developed to solve a real-world frequency assignment problem (FAP) in GSM networks. A precise mathematical formulation for this problem was used, where the frequency plans are evaluated using accurate interference information taken from a real GSM network. In this paper, our work is divided into two stages: In the first one, we have accurately tuned the algorithm parameters. Then, in the second step, we have compared the MO-ABC with previous versions of distinct multiobjective algorithms already developed to the same instances of the problem. As we will see, results show that this approach is able to obtain reasonable frequency plans when solving a real-world FAP. In the results analysis, we consider as complementary metrics the hypervolume indicator to measure the quality of the solutions to this problem as well as the coverage relation information.

TCSC allocation in power systems considering switching loss using MOABC algorithm

This paper is dedicated to investigate the influence of switching losses on Thyristor Controlled Series Compensator (TCSC) optimum allocation in power systems by using a Multi-Objective optimization technique. This allocation is performed with respect to minimization in system cost and voltage deviation as the objectives. For this purpose, cost of active power losses, investment cost of devices and active power generation cost in the peak load are considered to constitute the cost function as the economical objective. In addition, the paper considers interest rate in cost calculations to avoid impractical allocation results and imprecise solutions. The operational, technical and controlling constraints as well as load constraints are regarded in the allocation procedure. Also, the simulations are utilizing three unequal annual load levels to find optimum location and size of TCSC. The IEEE-14 Bus test system is used to validate the proposed method and show the importance of TCSC switching loss. Here, a novel optimization algorithm, Multi-Objective Artificial Bee Colony, is presented to provide Pareto optimal solutions. Also, an approach based on the goal attainment method combined with Genetic Algorithm is used to compromise between contradictory objectives and approach to the global optimum.

Multiobjective metaheuristics for frequency assignment problem in mobile networks with large‐scale real‐world instances

PurposeThe purpose of this paper is to address a multiobjective FAP (frequency assignment problem) formulation. More precisely, two conflicting objectives – the interference cost and the separation cost – are considered to characterize FAP as an MO (multiobjective optimization) problem.Design/methodology/approachThe contribution to this specific telecommunication problem in a real scenario follows a recent approach, for which the authors have already accomplished some preliminary results. In this paper, a much more complete analysis is performed, including two well‐known algorithms (such as the NSGA‐II and SPEA2), with new results, new comparisons and statistical studies. More concretely, in this paper five different algorithms are presented and compared. The popular multiobjective algorithms, NSGA‐II and SPEA2, are compared against the Differential Evolution with Pareto Tournaments (DEPT) algorithm, the Greedy Multiobjective Variable Neighborhood Search (GMO‐VNS) algorithm and its variant Greedy Multiobjective Skewed Variable Neighborhood Search (GMO‐SVNS). Furthermore, the authors also contribute with a new design of multiobjective metaheuristic named Multiobjective Artificial Bee Colony (MO‐ABC) that is included in the comparison; it represents a new metaheuristic that the authors have developed to address FAP. The results were analyzed using two complementary indicators: the hypervolume indicator and the coverage relation. Two large‐scale real‐world mobile networks were used to validate the performance comparison made among several multiobjective metaheuristics.FindingsThe final results show that the multiobjective proposal is very competitive, clearly surpassing the results obtained by the well‐known multiobjective algorithms (NSGA‐II and SPEA2).Originality/valueThe paper provides a comparison among several multiobjective metaheuristics to solve FAP as a real‐life telecommunication engineering problem. A new multiobjective metaheuristic is also presented. Preliminary results were enhanced with two well‐known multiobjective algorithms. To the authors' knowledge, they have never been investigated for FAP.

A multi-objective artificial bee colony algorithm

This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto front maintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutions maintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work.

A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production

To achieve burdening process optimization of copper strips effectively, a nonlinear constrained multi-objective model is established on the principle of the actual burdening. The problem is formulated with two objectives of minimizing the total cost of raw materials and maximizing the amount of waste material thrown into melting furnace. In this paper, a novel approach called “hybrid multi-objective artificial bee colony” (HMOABC) to solve this model is proposed. The HMOABC algorithm is new swarm intelligence based multi-objective optimization technique inspired by the intelligent foraging behavior of honey bees, summation of normalized objective values and diversified selection (SNOV-DS) and nondominated sorting approach. Two test examples were studied and the performance of HMOABC is evaluated in comparison with other nature inspired techniques which includes nondominated sorting genetic algorithm II (NSGAII) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate HMOABC approach is a powerful search and optimization technique for burdening optimization of copper strips.