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Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems. [ABSTRACT FROM AUTHOR]

Butterfly optimization algorithm is a population intelligent optimization algorithm, which is easy to fall into local optimal and has low searching accuracy. In this paper, a chaotic adaptive butterfly optimization algorithm (CABOA) is proposed. Firstly, the Kent mapping is used to initialize the location of individual butterfly and increase the diversity of initial population. Secondly, adaptive switch probability is introduced in search phases to balance local search and global search phase. Then, the adaptive weight coefficient is introduced to adjust the search direction and the moving step of the individual butterfly to improve the convergence speed and accuracy of the algorithm. Finally, Cauchy variation is carried out to guide the advance direction of the population and avoid falling into local optimum. In this paper, the algorithm is used to obtain the optimal value of 12 benchmark test functions, and is applied to the design of welded beams. Experimental results show that the performance of the improved algorithm has significant advantages over other comparison algorithms: in the function optimization experiment, the average optimal value is improved by 99% compared with the original algorithm, and about 90% compared with other improved algorithms. Meanwhile, the algorithm proposed in this paper obtains the optimal value of all functions and has high efficiency. Compared with other existing optimization algorithms, CABOA has better convergence performance and certain competitiveness, and has certain advantages in the application field. [ABSTRACT FROM AUTHOR]

Seepage prediction is a vital part of the dam safety monitoring system. Traditional statistical models ignore the nonlinear characteristics of the measured variables, resulting in poor accuracy and stability. In this study, an optimized neural network model is constructed to predict the seepage extent of hydropower station dams. An improved gradient-based optimizer (IGBO) is proposed to increase the accuracy and reliability of extreme learning machine (ELM) model predictions. The IGBO introduces an initialization method with elite opposition-based learning to improve population diversity. A crossover operator and a nonlinear parameter are used in the IGBO to enhance the ability of local search and the probability of avoiding local optima. The performance of the IGBO-optimized ELM network (IGBO-ELM) was evaluated on 12 datasets. In addition, the comparison experimental results with actual monitoring data of concrete dams show that IGBO-ELM has strong generalization performance and accuracy among the other four optimization models. [ABSTRACT FROM AUTHOR]

This paper proposes a firefighting multi strategy marine predators algorithm(FMMPA), which uses opposition-based learning (OBL) to make the initial population more uniform, introduces adaptive weight factors to balance exploration ability and exploitation ability, and combines DE/RAND/1 mutation mechanism to increase population diversity. Another purpose of our FMMPA is to add fire weighted selection, including flame edge suppression, combustion unit continuity and wind direction, as the rescue algorithm of forest fire rescue assemble to deal with the allocation of firefighting aircraft in forest fire rescue. We conduct this study based on real maps and the capabilities of the real firefighting aircraft. Compared with ten algorithms, the experimental results show that the proposed FMMPA has superior performance in reducing rescue time, controlling the spread speed of fire edge and minimizing loss cost. [ABSTRACT FROM AUTHOR]

The hybrid models of granular computing and support vector machine are a kind of new machine learning algorithms based on granular computing and statistical learning theory. These hybrid models can effectively use the advantage of each algorithm, so that their performance are better than a single method. In view of their excellent learning performance, the hybrid models of granular computing and support vector machine have become one of the focus at home and abroad. In this paper, the research on the hybrid models are reviewed, which include fuzzy support vector machine, rough support vector machine, quotient space support vector machine, rough fuzzy support vector machine and fuzzy rough support vector machine. Firstly, we briefly introduce the typical granular computing models and the basic theory of support vector machines. Secondly, we describe the latest progress of these hybrid models in recent years. Finally, we point out the research and development prospects of the hybrid algorithms. [ABSTRACT FROM AUTHOR]

Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects. [ABSTRACT FROM AUTHOR]