[Show abstract][Hide abstract]ABSTRACT: Multi-objective optimization problems with interval parameters (IMOPs) are ubiquitous in real-world applications. The existing evolutionary algorithms for IMOPs (IMOEAs) require a large amount of function evaluations to generate an approximate Pareto front which is well converged and evenly distributed, and the generated front has uncertainties to a large extent. In this paper, a local search is embedded into an existing IMOEA, and a memetic algorithm for IMOPs is developed. The existing IMOEA is first employed to search the entire search space, and then the rate of changes of hypervolume is utilized to design an activation mechanism to specify when to conduct the local search. Finally, an initial population of the local search is created by taking the individuals with a large contribution to hypervolume and a small imprecision as the center, and the local search is implemented by taking the contribution to hypervolume as its fitness function. The proposed algorithm is applied to six benchmark IMOPs and an uncertain optimization problem of solar desalination, and compared with a typical IMOEA without the local search. The empirical results indicate the effectiveness of the proposed algorithm.
[Show abstract][Hide abstract]ABSTRACT: Interval programming problems are ubiquitous in real-world situations. There exist a variety of theories and methods for handling them; the existing methods, however, have adopted various dominance criteria to distinguish solutions, and these criteria are always subjective. Different dominance criteria will produce different optimal solution(s), and subjective criteria make users, especially for those who are not familiar with interval arithmetic, difficult to choose, which restricts their widespread applications. In this study, the idea of ensemble dominance on intervals for tackling these problems is proposed. Dominance criteria on intervals are first defined, and their correlations are depicted by equivalent, inclusive and non-included relations; then, a reduction scheme is derived by investigating the influence of different criteria on the ordering of solutions, and a novel ensemble dominance relation on intervals is defined to rationally and equally evaluate the quality of a solution; furthermore, the complexity of the proposed method is analyzed; finally, empirical results indicate the effectiveness of the proposed method.
[Show abstract][Hide abstract]ABSTRACT: Context: As a fault-based testing technique, mutation testing is effective at evaluating the quality of existing test suites. However, a large number of mutants result in the high computational cost in mutation testing. As a result, mutant reduction is of great importance to improve the efficiency of mutation testing.
Objective: We aim to reduce mutants for weak mutation testing based on the dominance relation between mutant branches.
Method: In our method, a new program is formed by inserting mutant branches into the original program. By analyzing the dominance relation between mutant branches in the new program, the non-dominated one is obtained, and the mutant corresponding to the non-dominated mutant branch is the mutant after reduction.
Results: The proposed method is applied to test ten benchmark programs and six classes from open-source projects. The experimental results show that our method reduces over 80% mutants on average, which greatly improves the efficiency of mutation testing.
Conclusion: We conclude that dominance relation between mutant branches is very important and useful in reducing mutants for mutation testing.
Article · May 2016 · Information and Software Technology
[Show abstract][Hide abstract]ABSTRACT: The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.
Article · Apr 2016 · International Journal of Production Research
[Show abstract][Hide abstract]ABSTRACT: A blocking lot-streaming flow shop scheduling problem with interval processing time has a wide range of applications in various industrial systems, however, not yet been well studied. In this paper, the problem is formulated as a multi-objective optimization problem, where each interval objective is converted into a real-valued one using a dynamically weighted sum of its midpoint and radius. A novel evolutionary multi-objective optimization algorithm is then proposed to solve the re-formulated multi-objective optimization problem, in which non-dominated solutions and differences among parents are taken advantage of when designing the crossover operator, and an ideal-point assisted local search strategy for multi-objective optimization is employed to improve the exploitation capability of the algorithm. To empirically evaluate the performance of the proposed algorithm, a series of comparative experiments are conducted on 24 scheduling instances. The experimental results show that the proposed algorithm outperforms the compared algorithms in convergence, and is more capable of tackling uncertainties.
Full-text Article · Feb 2016 · Applied Soft Computing
[Show abstract][Hide abstract]ABSTRACT: Set-based evolutionary optimization based on the performance indicators is one of the effective methods to solve many-objective optimization problems; however, previous researches didn’t make full use of the preference information of a high-dimensional objective space to guide the evolution of a population. In this study, we propose a set-based many-objective evolutionary optimization algorithm guided by preferred regions. In the mode of set-based evolution, the proposed method dynamically determines a preferred region of the high-dimensional objective space, designs a selection strategy on sets by combining Pareto dominance relation on sets with the above preferred region, and develops a crossover operator on sets guided by the above preferred region to produce a Pareto front with superior performance. The proposed method is applied to four benchmark many-objective optimization problems, and the experimental results empirically demonstrate its effectiveness.
[Show abstract][Hide abstract]ABSTRACT: A crucial task of software testing is the generation of high-quality test data, so as to find defects and errors during various periods of software development. However, existing coverage-based testing methods seldom consider the fault finding ability of the test data. This paper establishes a constrained multi-objective model of test data generation, so that the generated test suite has better spatial distribution on the basis of satisfying statement coverage criterion, and thereby enhance its error detection ability. In addition, the authors propose a genetic algorithm (GA) based on set evolution to solve the model. The experimental results show that the test data generated by the proposed model have higher fault finding ability than statement coverage testing and adaptive random testing; in addition, compared with conventional GAs, the proposed algorithm needs less execution time with the number of test data not increasing significantly.
[Show abstract][Hide abstract]ABSTRACT: Hybrid interval multi-objective optimization problems are common in real-world applications. These problems involve both explicit and implicit objectives, and the values of these objectives are intervals. Few previous methods are suitable for them. An evolutionary algorithm with a large population and a user’s interval preferences was presented to effectively solve the problems in this paper. In the proposed algorithm, a similarity-based strategy was employed to estimate the interval values of implicit objectives of evolutionary individuals that the user had not evaluated in order to alleviate user fatigue; the user’s preferences to different objectives were expressed precisely as intervals by solving an auxiliary optimization problem; a sorting scheme based on the user’s preferences was proposed to guide the population evolving toward the user’s preferred regions. We applied the proposed method to an interior layout problem, which is a typical optimization problem with both interval parameters in the explicit objective and interval value of the implicit objective. The proposed algorithm was compared with four other optimization algorithms on the interior layout problem. Experimental results validated its effectiveness and superiority over the compared algorithms in terms of solution quality and the number of user’s evaluations.
[Show abstract][Hide abstract]ABSTRACT: Multi-objective particle swarm optimization (MOPSO) has been well studied in recent years. However, existing MOPSO methods are not powerful enough when tackling optimization problems with more than three objectives, termed as many-objective optimization problems (MaOPs). In this study, an improved set evolution multi-objective particle swarm optimization (S-MOPSO, for short) is proposed for solving many-objective problems. According to the proposed framework of set evolution MOPSO (S-MOPSO), including quality indicators-based objective transformation, the Pareto dominance on sets, and the particle swarm operators for set evolution, an enhanced S-MOPSO method is developed by updating particles hierarchically, i.e., a set of solutions is first regarded as a particle to be updated and then the solutions in a selected set are further evolved by a modified PSO. In the set evolutionary stage, the strategy for efficiently updating the set particle is proposed. When further evolving a single solution in the initial decision space of the optimized MaOP, the global and local best particles are dynamically determined based on those ideal reference points. The performance of the proposed algorithm is empirically demonstrated by applying it to several scalable benchmark many-objective problems.
[Show abstract][Hide abstract]ABSTRACT: Based on the methodology of bio-inspired control of humanoid robot, human motion capture data is crucially important for improving the quality of gait as well as the ability of push-recovery of the humanoid. In order to get the human motion data that is easy to be applied to humanoid motion control, we designed a wearable human motion capture system that can capture the motion of the trunk and the lower limbs in the joint space as well as the motion of the pelvis in the Descartes space; and the forces exerted on the sole of foot are also measured so as to capture the moving of the zero moment point (ZMP) of human when he/she is walking. Both the mechanical and electronic parts of the motion capture system are introduced in detail; the advantages and the disadvantages of the system are also discussed.
[Show abstract][Hide abstract]ABSTRACT: Many-objective optimization problems widely exist in real world. However, there is lack of effective solutions to solve this problem because they contain more than three conflicting objectives. Based on quantum particle swarm optimization algorithm, this paper presents an efficient many-objective particle swarm optimization algorithm. In this algorithm, an improved quantum update method is introduced to update the particles' positions, a selection strategy based on the global difference order is proposed to update the global best position of particle, and a congestion sorting strategies is applied to update the external repository. By optimizing ZDT and DTLZ series functions, and comparing with representative algorithms such as TV-MOPSO, results indicate that the proposed algorithm is effective for solving many-objective optimization problems.
[Show abstract][Hide abstract]ABSTRACT: Generating test data by genetic algorithms is a promising research direction in software testing, among which path coverage is an important test method. The efficiency of test data generation for multi-path coverage needs to be further improved. We propose a test data generation method for multi-path coverage based on a genetic algorithm with local evolution. The mathematical model is established for all target paths, while in the algorithm the individuals are evolved locally according to different objective functions. We can improve the utilization efficiency of test data. The computation cost can be reduced by using fitness functions of different granularity in different phases of the algorithm.
Article · Mar 2015 · Chinese Journal of Electronics
[Show abstract][Hide abstract]ABSTRACT: User fatigue significantly restricts the practical applications of interactive genetic algorithms for complicated optimization problems. To alleviate user fatigue, an interactive genetic algorithm is presented in this study, where individuals are evaluated with varying accuracy. In the developed algorithm, multiple language sets with different granularities are proposed and applied to evaluate individuals. A subset of the whole language sets is chosen first to evaluate a population, adaptively to the convergence of the current population. For an individual in the current population, an appropriate language set is chosen from the subset to evaluate it according to the distance between the individual and the user’s preferred region. The proposed algorithm is compared with some other algorithms in literature on curtain design. Empirical results demonstrate that the developed algorithm can significantly alleviate user fatigue and improve search efficiency.
[Show abstract][Hide abstract]ABSTRACT: Feature selection is a useful pre-processing technique for solving classification problems. As an almost parameter-free optimization algorithm, the bare bones particle swarm optimization (BPSO) has been applied to the topic of optimization on continuous or integer spaces, but it has not been applied to feature selection problems with binary variables. In this paper, we propose a new method to find optimal feature subset by the BPSO, called the binary BPSO. In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm. Moreover, the 1-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. Some international standard data sets are selected to evaluate the proposed algorithm. The experiments show that the proposed algorithm is competitive in terms of both classification accuracy and computational performance.
[Show abstract][Hide abstract]ABSTRACT: A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.
[Show abstract][Hide abstract]ABSTRACT: Research on complex networks is becoming a very hot topic in recent years, among which node matching problem is an important issue. The aim of node matching problem is to find out the corresponding relations between the individuals of associated networks. Traditional node matching problem of networks always hypothesize that a proportion of matching nodes are known. However, if the ratio of matched nodes is very small, the matching accuracy of the remaining nodes cannot be evaluated accurately. What is more, we may have not any matched nodes for reference at all. In view of this, this paper established the mathematic model of node matching problem based on the adjacency matrixes of networks, and presented an evolutionary algorithm to solve it. The experimental results show that the proposed method can achieve satisfactory matching precision in the absence of any matched nodes.
Article · Nov 2014 · Physica A: Statistical Mechanics and its Applications
[Show abstract][Hide abstract]ABSTRACT: Employing genetic algorithms to generate test data for path coverage has been an important method in software testing. Previous work, however, is suitable mainly for serial programs. Automatic test data generation for path coverage of message-passing parallel programs without non-determinacy is investigated in this study by using co-evolutionary genetic algorithms. This problem is first formulated as a single-objective optimization problem, and then a novel co-evolutionary genetic algorithm is proposed to tackle the formulated optimization problem. This method employs the alternate co-evolution of two kinds of populations to generate test data that meet path coverage. The proposed method is applied to seven parallel programs, and compared with the other three methods. The experimental results show that the proposed method has the best success rate and the least number of evaluated individuals and time consumption.
Article · Nov 2014 · Automated Software Engineering
[Show abstract][Hide abstract]ABSTRACT: Feature selection is an important data preprocessing technique in classification problems. This paper focuses on a new feature selection problem, in which sampling data of different features have different reliability degree. First, the problem is modeled as a multi-objective optimization. There two objectives should be optimized simultaneously: reliability and classifying accuracy of feature subset. Then, a multi-objective feature selection method based on particle swarm optimization, called JMOPSO, is proposed by incorporating several effective operators. Finally, experimental results suggest that the proposed JMOPSO is a highly competitive feature selection method for solving the feature selection problem with unreliable data.