LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

It is one of the crucial problems in solving multi-objective problems (MOPs) that balance the convergence and diversity of the algorithm to obtain an outstanding Pareto optimal solution set. In order to elevate the performance further and improve the optimization efficiency of multi-objective particle swarm optimization (MOPSO), a novel adaptive MOPSO using a three-stage strategy (tssAMOPSO) is proposed in this paper, which can effectively balance the exploration and exploitation of the population and facilitate the convergence and diversity of MOPSO. Firstly, an adaptive flight parameter adjustment, formulated by the convergence contribution of nondominated solutions, can ameliorate the convergence and diversity of the algorithm enormously. Secondly, the population carries out the three-stage strategy of optimization in each iteration, namely adaptive optimization, decomposition, and Gaussian attenuation mutation. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Moreover, the convergence of three-stage optimization strategy is analyzed. Then, memory interval is equipped with particles to record the recent positions, which vastly improves the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of nondominated solutions directly. Finally, comparative experiments are designed by a series of benchmark instances to verify the performance of tssAMOPSO. Experimental results show that the proposed algorithm achieves admirable performance compared with other contrast algorithms.

Multiobjective Optimization of Cable Forces and Counterweights for Universal Cable-Stayed Bridges

In cable-stayed bridges, especially asymmetric bridges, counterweights are always made to work together with cable pretension forces to get a reasonable finished state. To solve the optimization problem of the cable-stayed bridge considering the counterweights, the integrated optimization method (IOM) for estimating cable forces and counterweights is proposed. In this method, the counterweights are proposed to act on the anchor points. After that, the summary of the minimum weighted total bending energy and the summary of the counterweights are considered as two objective functions of a multiobjective problem. Finally, the dynamic weighted coefficient method is used to solve this problem and realize the Pareto solution set. IOM presents detailed procedures in a simple numerical model and is then applied to the Yong-ding special-shaped cable-stayed bridge. The results show that not only IOM can realize the priority selection of the loading position of the counterweights but also get a better reasonable finish state because of the introduction of the counterweight dimension; the dynamic weighted coefficient method can quickly find the Pareto optimal solution set and be further screened by decision-makers; counterweight is very helpful to reduce torsion and other spatial effects in cable-stayed bridges. IOM can be used as a universal optimization method for cable-stayed bridges.

Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization

<p style='text-indent:20px;'>The microgrid technology, which can dispatch power independently, is an effective way to increase the efficiency of energy utilization meanwhile develop and utilize the clean and renewable energy. However, the power generation of a single microgrid is unstable, because it is greatly affected by the external environment. Therefore, the development and application of the multi-microgrid system have gradually drawn various countries' attention. In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective complex constrained optimization model, using interval optimization to handle uncertainties of renewable energies. In view of the model characteristics, the metaheuristic strategies about initialization and repair of solution are designed. Furthermore, the fuzzy membership degree and Chebyshev function are used in parallel to decompose the multi-objective optimization problem, thus a multi-objective evolutionary algorithm based on hybrid decomposition (MOEA/HD) is constructed. Finally, the effectiveness of the metaheuristic strategies can be verified by analyzing the simulation results in this paper. Moreover, the results prove that the MOEA/HD is more efficient, which can get a higher-quality Pareto optimal solution set when compared to other algorithms.</p>

Proximity measures based on KKT points for constrained multi-objective optimization

An important aspect of optimization algorithms, for instance evolutionary algorithms, are termination criteria that measure the proximity of the found solution to the optimal solution set. A frequently used approach is the numerical verification of necessary optimality conditions such as the Karush–Kuhn–Tucker (KKT) conditions. In this paper, we present a proximity measure which characterizes the violation of the KKT conditions. It can be computed easily and is continuous in every efficient solution. Hence, it can be used as an indicator for the proximity of a certain point to the set of efficient (Edgeworth-Pareto-minimal) solutions and is well suited for algorithmic use due to its continuity properties. This is especially useful within evolutionary algorithms for candidate selection and termination, which we also illustrate numerically for some test problems.

Selection model of work technology based on multi–criteria evaluations

A model for the selection of options for the production of work in a construction project is considered, when each option is characterized by a set of criteria. The number of analyzed options is being reduced based on the construction of the Pareto-optimal solution set. The remaining options are used to solve the problem based on the network model,\ in which the solution will be a subcritical path that meets budgetary constraints. At the same time, the proposed comprehensive indicator characterizing the preferences of the customer makes it possible to determine alternative options for performing work in the energy project in such a way that the amount of costs allocated to implement the set of work under consideration is minimal. Another statement of the problem is also considered when it is necessary to determine a strategy for the implementation of an energy project that, given a planned budget constraint, maximizes the growth of a comprehensive indicator that characterizes customer preferences in this project. The solution of the tasks is given under the assumption of the convexity of the cost function.