This paper presents an innovative approach employing persistence-based clustering in Riemannian manifolds within evolutionary computation algorithms to address multi-modal optimization problems. The proposed framework is im-plemented and evaluated using the chaotic evolution algorithm. We introduce a novel algorithm named chaotic evolution with a clustering algorithm (CECA), which integrates the chaotic evolution characteristics from chaotic systems with the clustering method and Gaussian local search to solve multi-modal optimization problems. By leveraging chaotic dynamics, CECA enhances exploration and exploitation for efficient searching. Simultane-ously, it utilizes the clustering method to improve population diversity in the context of multi-modal optimization problems. The effectiveness and advantages of the proposed framework on the CECA algorithm are demonstrated through extensive experimental evaluations of various benchmark functions, in-cluding the Congress on Evolutionary Computation (CEC) con-ference functions. The experimental results indicate that the proposed framework exhibits distinct advantages in optimizing high-dimensional complex multi-modal functions. This study provides empirical evidence that persistence-based clustering in Riemannian manifolds constitutes an effective methodology for evolutionary multi-modal optimization.

Genetic algorithms have played an important role in engineering optimization. Traditional GAs treat each gene separately. However, biophysical studies of gene regulatory networks revealed direct associations between different genes. It inspires us to propose an improvement to GA in this paper, Gene Regulatory Genetic Algorithm (GRGA), which, to our best knowledge, is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We design a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes. The edge's weight reflects the relationship degree and is updated based on the idea that the edges' weights in a complete chain as candidate solution with acceptable or unacceptable performance should be strengthened or reduced, respectively. The obtained RGGR is then employed to determine appropriate loci of crossover and mutation operators, thereby directing the evolutionary process toward faster and better convergence. We analyze and validate our proposed GRGA approach in a single-objective multimodal optimization problem, and further test it on three types of applications, including feature selection, text summarization, and dimensionality reduction. Results illustrate that our GARA is effective and promising.

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60–80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model.

This paper proposes a novel population-based optimization algorithm to solve the multi-modal optimization problem. We call it the chaotic evolution deterministic crowding (CEDC) algorithm. Since the genetic algorithm is difficult to find all optimal solutions and the accuracy is not high when searching for multi-modal optimization problems, we use the ergodicity of chaos to implement the exploration and fitness comparison of the deterministic crowding algorithm. Through the tests of several multi-modal benchmark functions, it is shown that the algorithm can effectively and accurately find the most optimal solutions to the multi-modal problem. It does not need to set the niche radius in advance, so it can better solve multimodal optimization problems. We test it with nine multi-modal benchmark functions ranging from one-dimension (1-D) to ten-dimension (10-D), and we compare it with a genetic algorithm and evaluate from peak ratio, max peak ratio, and running time. The experimental results show that the CEDC algorithm is better than conventional algorithms in both runtime and peak ratio.

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner.

Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

Optimization is the process of finding and comparing feasible solutions and adopting the best one until no better solution can be found. Because solving real-world problems often involves simulations and multiobjective optimization, the results and solutions of these problems are conceptually different from those of single-objective problems. In single-objective optimization problems, the global optimal solution is the solution that yields the optimal value of the objective function. However, for multiobjective optimization problems, the optimal solutions are Pareto-optimal solutions produced by balancing multiple objective functions. The strategic variables calculated in multiobjective problems produce different effects on the mapping imbalance and the search redundancy in the search space. Therefore, this paper proposes a fitness landscape ruggedness multiobjective differential evolution (LRMODE) algorithm with a reinforcement learning strategy. The proposed algorithm analyses the ruggedness of landscapes using information entropy to estimate whether the local landscape has a unimodal or multimodal topology and then combines the outcome with a reinforcement learning strategy to determine the optimal probability distribution of the algorithm’s search strategy set. The experimental results show that this novel algorithm can ameliorate the problem of search redundancy and search-space mapping imbalances, effectively improving the convergence of the search algorithm during the optimization process.

The traditional particle swarm optimization algorithm learns from the two best experiences: the best position previously learned by the particle itself and the best position learned by the entire population to date. This learning strategy is simple and ordinary, but when addressing high-dimensional optimization problems, it is unable to quickly find the global optimal solution due to its low efficiency. This paper proposes a multipopulation cooperative particle swarm optimization (MPCPSO) algorithm with a dynamic segment-based mean learning strategy and a multidimensional comprehensive learning strategy. In MPCPSO, the dynamic segment-based mean learning strategy (DSMLS), which is employed to construct learning exemplars, achieves information sharing and coevolution between populations. The multidimensional comprehensive learning strategy (MDCLS) is employed to speed up convergence and improve the accuracy of MPCPSO solutions. Additionally, a differential mutation operator is introduced to increase the population diversity and enhance the global exploration ability of MPCPSO. Sixteen benchmark functions and seven well-known PSO variants are employed to verify the advantages of MPCPSO. The comparison results indicate that MPCPSO has a faster convergence speed, obtains more accurate solutions, and is more robust.

Rail guide vehicle (RGV) dynamic scheduling problems have attracted increasing attention in recent years, which determines a great impact on the working efficiency of the entire scheduling system. However, the relative intelligent optimization study of RGV dynamic scheduling problems are insufficient scheduling of different working components in the previous works, it is easy to appear idle waiting, resulting in reduced operating efficiency during operation. Analysis of the fitness landscape is essential to understand the behavior of evolutionary algorithms for solving dynamic optimization problems in the evolutionary dynamics of biological evolution. With the continuous advancement of evolutionary algorithm optimization, the fitness landscape can present more abundant feature information around the fitness value, including autocorrelation, fitness distance correlation, landscape walks, local optima, and landscape roughness. This paper proposes a new distance landscape strategy for the RGV dynamic scheduling problems. The combination of the fitness landscape and dynamic search strategy are established according to the operating principle of the RGV system. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with one procedure programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solve the considered RGV dynamic scheduling problems effectively.

The particle swarm optimization (PSO) algorithm has been abroad applied in many tasks due to its merits, such as simple structure and easy to implement. However, PSO tends to converge prematurely, and the results are usually not satisfactory when addressing complex problems. This paper proposes a novel hybrid optimization technique called differential learning particle swarm optimization with full dimensional information (FDLPSO). In FDLPSO, the differential search method is adopted to enhance diversity and avoid getting trapped in a local optimal solution. Unlike the traditional PSO algorithm, in order to improve the comprehensive search capabilities of the algorithm, a full dimensional information learning strategy is proposed to construct learning exemplars to guide the learning of the particles and the convergence and adaptability of FDLPSO. The comparison results show that FDLPSO has a superior performance than seven well-known PSO variants and can quickly find a more competitive solution.