Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Multiple sequences alignment is an essential task in many bioinformatics analyses. However, it is a challenging problem due to its exponential complexity. Although many approaches have been proposed for solving the MSA problem, finding the best alignment of the MSA remains an open problem. In this work, we propose a new multiobjective optimization approach based on the Artificial Bee Colony (ABC) algorithm to solve the MSA problem. The ABC algorithm is inspired by the intelligent behavior of honey bee swarms. Our proposed approach optimizes two objective functions; the sum-of-pairs (SP) function and entropy. Optimizing these objective functions represents a trade off between maximizing the SP function and minimizing the entropy to choose the most suitable alignment. Experiments are conducted on 12 datasets from BAliBASE 3.0. The experimental results show that our proposed approach achieves better alignments than Clustal in four of the tested datasets. In general, the proposed approach performs better on RV12 datasets.

Multiobjective optimization is successfully applied in many biological problems. Currently, most biological problems require to optimize more than one single objective at the same time, resulting in Multiobjective Optimization Problems (MOP). In the last years, multiple metaheuristics have been successfully used to solve optimization problems. However, many of them are designed to solve problems with only one objective function. In this work, we study several multiobjective adaptations to solve one of the most important biological problems, the Motif Discovery Problem (MDP). MDP aims to discover novel Transcription Factor Binding Sites (TFBS) in DNA sequences, maximizing three conflicting objectives: motif length, support, and similarity. For this purpose, we have used the Artificial Bee Colony algorithm, a novel Swarm Intelligence algorithm based on the intelligent behavior of honey bees. As we will see, the use of one or another multiobjective adaptation causes significant differences in the results.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.

Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. ABC has gained increasing attention in tackling optimization problems. Its further superiority when hybridized with other techniques is also shown. In this paper a novel hybrid artificial bee colony is proposed in order to solve optimization problems more efficiently, accurately and reliably. During the course of evolvement Extremal Optimization is used to improve the search performance and this makes proposed algorithms have more powerful exploitation capabilities. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.

Due to the worldwide growth of energy consumption, analysis of energy issues and the development of energy policy options has become an important issue. In this study, electricity energy consumption of Turkey is predicted by artificial bee colony algorithm (ABC) approaches. ABC, a recently proposed swarm based algorithm, models the intelligent foraging behavior of honey bee swarms. ABC algorithm is used to develop linear and quadratic models as well to train artificial neural network models. The proposed approaches predict Turkey’s net electricity energy consumption until 2022 according to inputs from the year 1979 according to three scenarios. The data used in this study is collected from the Ministry of Energy and Natural Resources (MENR) of Turkey and Turkish Statistical Institute.

Grain is an important economic and strategic material of the country. In grain transportation, it is necessary to consider the running time, vehicle number, path length and other factors at the same time, which is a typical multi-objective problem, but also a NP-Hard problem. In this paper, an Artificial Bee Colony algorithm is introduced to solve the routing problem of grain transportation vehicles with multi-objective and time windows. Combined with the practical problems of grain transportation, the standard Artificial Bee Colony algorithm is improved in four aspects: population initialization, domain search mode, bulletin board setting and scout bee search mode, and a Multi-objective Artificial Bee Colony algorithm is proposed by using the strategy of first classification and then sorting and final iteration. The proposed algorithm is compared with other algorithms by using the standard test set in Solomon database. The results show that the Multi-objective Discrete Artificial Bee Colony algorithm has great advantages in solving the routing problem of grain transportation vehicles with time windows.

Targeting at the parameters reconfiguration of a cognitive radio system, a novel cognitive decision engine (CDE), based on an improved multi-objective artificial bee colony (IMOABC) algorithm and f...

A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems

Artificial Bee Colony Algorithm is a nature-inspired evolutionary algorithm that imitates the intelligent behavior of honey bee swarm. It is one of the recent algorithms that imitate the foraging behavior of bees. A bee swarm optimization to solve various numerical problems has been presented in this paper. The paper describes an algorithm for implementation of different mathematical problems, such as single integration, eigen value and vector, etc., using Artificial Bee Colony Algorithm. Results with different number of bees are shown and best suited range of the number of bees, giving best solution to solve all problems has been shown and concluded. The results have shown that the Artificial Bee Colony algorithm has helped to achieve a near optimum solution of the problem defined and thus is successful in solving the mathematical problems with good accuracy.

In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. According to the characteristics of the model, initial processing sequence is used as solution vector in order to narrow down feasible solutions. Fitness of populations is distinguished by non-dominated sorting. In the process of iteration, excellent individuals are retained so that the diversity of population distribution is increased. Finally, the method is applied to a simulation example, compared with the traditional multi-objective algorithm. The results obtained demonstrate that the improved ABC algorithm for hybrid flow shop scheduling problem is good effective and diversified.

Artificial Bee Colony algorithm is a very powerful Swarm Intelligence Algorithm that has been applied in a number of different kind of optimization problems since the time that it was published. In recent years there is a growing number of optimization models that trying to reduce the energy consumption in routing problems. In this paper, a new variant of Artificial Bee Colony algorithm, the Parallel Multi-Start Multiobjective Artificial Bee Colony algorithm (PMS-ABC) is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). In the formulation four different scenarios are proposed where the distances between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with three other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Differential Evolution (PMS-NSDE), the Parallel Multi-Start Non-dominated Sorting Particle Swarm Optimization (PMS-NSPSO) and the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II) in a number of benchmark instances.