Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.

https://www.mdpi.com/1999-4893/13/4/85/pdf

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

https://iopscience.iop.org/article/10.1088/1757-899X/734/1/012090/pdf

The research and development of the hybrid algorithm based on the collective behavior of Fish schools and the classical optimization methods

At this stage, the development of science and technology in China is extremely fast, and the research of computer artificial intelligence technology is the best interpretation of human wisdom. The algorithm research of artificial intelligence technology continues to penetrate into various aspects. Work evaluation is a kind of human resource management technology and method to determine the relative value of positions. It shows a certain degree of scientificity and objectivity in post evaluation and salary rating. It is widely used in enterprises, but there are also many problems in its use. This paper expounds the related concepts of artificial intelligence technology and job evaluation, and then analyzes the current situation and existing problems in the evaluation of enterprise work in the context of artificial intelligence, and proposes the design and implementation of the work evaluation monitoring system, which is more for the enterprise in practice. Provide a reference for good operational evaluation.

https://francis-press.com/uploads/papers/GdVL4i8BlAIuSahlvJAIjmsYFZFeGFleelYxPK4M.pdf

Design and Implementation of Work Evaluation Monitoring System under Artificial Intelligence Condition

In this paper, we propose a new approach for self-adaptive particle swarm optimization, using the function's topology to adapt the parameters and modifying them when a planar region is identified in the objective function. Particle swarm optimization is a metaheuristic developed to optimize nonlinear problems. This metaheuristic has four parameters to adapt the search for the different optimization problems. However, finding an optimal set of parameters is not a trivial problem. Some strategies to adapt the parameters have been developed, but they are not robust enough to cover all kinds of problems. Function's topology is one of the most decisive factors in order to choose a right set of parameters; i.e. convex functions need more exploitation because this topology offers a clear direction to the minimum point. In the opposite way, a noise function can be trapped in a local minimum for the same level of exploitation. In order to validate and compare our methods, we use the benchmark functions from CEC 2005 to compare the different particle swarm optimization versions. The results show that the proposed version is significant better than the original particle swarm optimization and the standard particle swarm optimization proposed in 2011.

A new self-adaptive PSO based on the identification of planar regions

The aim of this paper, is to present to those researchers and practitioners not familiarized with the design, evaluation and use of heuristic algorithms for non-restricted non-linear global optimization, a set of hints that facilitate and promote a better understanding of the difficulties associated with this problem and the aspects related to successful use of a specific heuristic algorithm. Aspects as the features of the objective function, sensibility to the values of the parameters, and the use of plots for analyzing the behavior of an algorithm are discussed.

Evaluation and implementation of heuristic algorithms for non-restricted global optimization

Metaheuristics are used successfully in several global optimization functions. Problems arise when functions have large flat regions since information, given by the slope, necessary to guide the search is insufficient. In such case, a common solution can be a change in the metaheuristic’s parameters in order to attain a optimal balance between the exploration and exploitation. In this paper, we propose a criterion to determine when a flat region can be problematic. It is validated with a very simple hybrid algorithm based on the use of PSO technique for optimizing non-flat regions and Monte Carlo sampling for searching the global optimum in large flat regions. The proposed criterion switches the both algorithms to provide more exploitation for descendent functions and more exploration for planar functions. Preliminary results show that the proposed hybrid algorithm finds better results than PSO and Monte Carlo techniques in isolation for ten well-known test functions.

Nonlinear Optimization in Landscapes with Planar Regions

In this paper, we present a novel methodology to generate complex functions using two-dimensional fuzzy sets as weights for combining classical benchmark functions. These new functions have different characteristics from original ones, but the minimum, borders and geometry characteristics of the functions are still known. Three different combinations of two functions (Rosenbrock and Bukin’s F4) are used to exemplify the method and its potential to generate specific test functions to study and improve optimization methods.

/pdf/generation-of-complex-nonlinear-benchmark-functions-for-4qwzk20hzh.pdf

Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions

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Papers

Evaluation and implementation of heuristic algorithms for non-restricted global optimization

Nonlinear Optimization in Landscapes with Planar Regions

Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions

A new self-adaptive PSO based on the identification of planar regions