An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence

The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed parameter are not universally applicable to problems and evolution stages with diverse characteristics; besides, the weakness of the advanced DE optimization framework, called selective-candidate framework with similarity selection rule (SCSS), is found by focusing on its single strategy and fixed parameter greedy degree (GD) setting. To address these problems, we mainly combine the multiple candidates generation with multi-strategy (MCG-MS) and the adaptive similarity selection (ASS) rule. On the one hand, in MCG-MS, two symmetrical mutation strategies, “DE/current-to-pbest-w/1” and designed “DE/current-to-cbest-w/1”, are utilized to build the multi-strategy to produce two candidate individuals, which prevents the over-approximation of the candidate in SCSS. On the other hand, the ASS rule provides the individual selection mechanism for multi-strategy to determine the offspring from two candidates, where parameter GD is designed to increase linearly with evolution to maintain diversity at the early evolution stage and accelerate convergence at the later evolution stage. Based on the advanced algorithm jSO, replacing its offspring generation strategy with the combination of MCG-MS and ASS rule, this paper proposes multi-strategy differential evolution algorithm with adaptive similarity selection rule (MSDE-ASS). It combines the advantages of two symmetric strategies and has an efficient individual selection mechanism without parameter adjustment. MSDE-ASS is verified under the Congress on Evolutionary Computation (CEC) 2017 competition test suite on real-parameter single-objective numerical optimization, and the results indicate that, of the 174 cases in total, it wins in 81 cases and loses in 30 cases, and it has the smallest performance ranking value, of 3.05. Therefore, MSDE-ASS stands out compared to the other state-of-the-art DEs.

The constrained multi-objective optimization problems (CMOPs) are always hard to handle when the constraints are too stringent. In this paper, a two-stage differential evolution algorithm (TSCMODE) is proposed to solve CMOPs, and non-dominated sorting is used to select offspring individuals. Firstly, in the early stage of evolution, a efficient constraint handing technique is proposed to generate more feasible solutions, and differential mutation is used to generate high quality offspring individuals. Then, at a later stage in evolution, the feasibility conditions are used to divide the population into upper and lower sub-populations, the good genes from the upper sub-population are passed to the lower offspring individuals by differential evolution. Besides, the objective values of the lower sub-population are adjusted self-adaptively to avoid the potential infeasibility being discarded. Finally, the proposed algorithm is executed on some recent benchmark functions and compared with four state-of-the art constrained multi-objective evolutionary algorithms. The experimental results show that TSCMODE can solve the CMOPs as well as.

Either in societies or economic cycles, the benefits of a group can be affected by various unpredictable factors. We study effects of additive spatiotemporal random variations on the evolution of cooperation by introducing them to the enhancement level of the spatial public goods game. Players are located on the sites of a two-dimensional lattice and gain their payoffs from games with their neighbors by choosing cooperation or defection. We observe that a moderate intensity of variations can best favor cooperation at low enhancement levels, which resembles classical coherence resonance. Whereas for high enhancement levels, we find that the random variations cannot increase the cooperation level, but hamper cooperation instead. This discrepancy is attributed to the different roles the additive variations played in the early and late stages of evolution. In the early stage of evolution, the additive variations increase the survival probability of the players with lower average payoffs. However, in the late stage of evolution, the additive variations can promote defectors to destroy the cooperative clusters that have been formed. Our results indicate that additive spatiotemporal noise may not be as universally beneficial for cooperation as the spatial prisoner's dilemma game.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Evolutionary optimized Padé approximation scheme for analysis of covid-19 model with crowding effect

In the late stage of the population evolution of the RNA genetic algorithm, mutation may destroy the individual genes with higher fitness, resulting in slower convergence or no optimal solution. Therefore, this paper proposes an RNA genetic algorithm that does not add mutation operations in the late stage of evolution (Referred to as nmlsRNA-GA), in the early stage of evolution, the goal is to find the approximate location of the optimal solution. The two-point crossover and fixed-probability mutation operations are performed in the crossover pool composed of individuals with high fitness and individuals with low fitness to increase the diversity of the population. In order to improve the global search ability of the algorithm. In the later stage of the evolution, the goal is to improve the local search ability to determine the optimal solution, and use individuals with high adaptability to cross over individuals with high adaptability to generate individuals with higher adaptability, thereby improving search Accuracy, no mutation operation is added at this stage to prevent mutation from destroying excellent genes. By optimizing five typical high-dimensional test functions, and comparing them with the RNA genetic algorithm that uses mutation throughout the process, the analysis results show that the algorithm has better performance.

In this paper, we presents a novel approach for solving optimal power flow (OPF) problems using a hybrid differential evolution and harmony search (DEHS). The DEHS method is an improved differential evolution method based on the harmony search scheme. Harmony Search has strong and easy to combine with other methods in optimization and the Differential Evolution algorithm has a very great ability to search solutions with a fast speed to converge, contrary to the most meta-heuristic algorithms. The DEHS method has the flexible adjustment of the parameters to get a better optimal solution. Moreover, an effective constraint handling framework in the method is employed for properly handling equality and inequality constraints of the problem. The proposed DEHS has been tested on three systems including IEEE-30 bus system with quadratic fuel cost function, IEEE-30 bus system with valve point effects fuel cost function and IEEE-57 bus system with quadratic fuel cost function. The obtained results from DEHS algorithm have been compared to those from other methods in the literature. The result comparison has indicated that the proposed DEHS method is more effective than many other methods for obtaining the optimal solution for the test systems. Therefore, the proposed DEHS is a very favorable method for solving the optimal power flow problems.

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. In terms of the objective function, most existing research has been focused on the makespan criterion. However, in contemporary manufacturing systems, due‐date‐related performances are more important because they are essential for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a hybrid differential evolution (DE) algorithm is proposed for the problem. To enhance the overall search efficiency, a neighborhood property of the problem is discovered, and then a tree search procedure is designed and embedded into the DE framework. According to the extensive computational experiments, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness objective.

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions.

A dynamic economic and environmental dispatch (DEED) problem is a challenging bi-objective optimization problem that simultaneously minimizes both operating costs and gas emissions. To solve it, several evolutionary algorithms (EAs) have been used, each of which has pros and cons, with one performing better in an early stage of evolution and another later. In this paper, to solve such problems, an evolutionary framework is designed based on two EAs, a genetic algorithm (GA) and differential evolution (DE), dynamically configures the better of the two during the evolution. In it, two sub-populations are performed, one for each of GA and DE, and their sizes updated in each generation according to the respective algorithm’s performance in previous generations. Moreover, a heuristic is employed to improve the performance of the proposed algorithm by repairing infeasible individuals towards feasible directions. To demonstrate its performance, two renewable-based DEED problems are solved using the proposed and state-of-the-art algorithms. An analysis of the simulation results reveals that the proposed algorithm is the best of those considered, with the heuristic enhancing its performances.

One of the most challenging problems in data clustering is to determine the number of clusters. This study intends to propose an improved differential evolution algorithm which integrates automatic clustering based differential evolution (ACDE) algorithm and k-means (ACDE-k-means) algorithm. It requires no prior knowledge about number of clusters. k-means algorithm is employed to tune cluster centroids in order to improve the performance of DE algorithm. To validate the performance of the proposed algorithm, two well-known data sets, Iris and Wine, are employed. The computational results indicate that the proposed ACDE-k-means algorithm is superior to classical DE algorithm.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

A hybrid differential evolution for optimal multilevel image thresholding

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

A hybrid differential evolution(DE) algorithm,namely OHTDE,is proposed to solve the stochastic flow shop scheduling problem(FSSP) with limited buffers between consecutive machines.Two different criteria are considered.The first is to minimize a general earliness/tardiness cost function and the second is to minimize the total completion time.The proposed algorithm is a hybrid of DE and two techniques:the optimal computing budget allocation(OCBA) technique and hypothesis test(HT) .DE is used to execute both global and local search.OCBA is utilized to reasonably allocate a limited sampling budget for every solution,assigning more for better individuals to improve the confidence level of getting good solutions under noise environment.HT is adopted to perform a statistical comparison on solutions performances so that the repeated search on the similar region of the solution space can be avoided to some extent.Moreover,a special crossover,which is performed on some good solutions identified by OCBA and HT,is designed to enhance the capability of local search.Furthermore,the stochastic convergence property of OHTDE is analyzed by using the theory of finite Markov chain.Experimental results and comparison show the effectiveness and robustness of the proposed OHTDE.