Ant Colony Particle Swarm Optimization Research Articles

Construction of preschool education resource sharing platform based on matrix model

Abstract This paper achieves high-quality sharing of preschool educational resources among learners based on a matrix model. Through the shared resource matrix analysis method, the symmetry of the matrix about this educational resource is judged so that the resource can transmit information implicitly among all subjects. The difference between noise and preschool education resource data points is enhanced using the K-nearest neighbor search algorithm. Combined with the ant colony particle swarm optimization algorithm, the sharing sequence and time of the optimal preschool education resources are calculated, and a preschool education resource-sharing platform is constructed. To verify the applicability of the constructed platform, the simulation analysis results show that the resource search accuracy of the platform constructed in this paper reaches 94.51% on average, and the compatibility performance index is as high as 7.287vw, which is 3.25vw and 3.421vw higher compared to other platforms, respectively. The results show that the matrix model can eliminate resource heterogeneity and provide convenient and efficient interactive technical support for constructing a preschool education resource-sharing platform.

A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty

In the context of a rapidly evolving integrated energy system (IES), taking into account the uncertainty of the renewable energy output can make integrated energy system planning more realistic. In view of this, this paper proposes an integrated energy system planning approach that takes uncertainty into account. Firstly, in order to accurately describe the renewable energy output scenarios, this paper describes the IES model and introduces the scenario analysis methods used. Secondly, an integrated energy system equipment output model is constructed, the corresponding constraints and objective functions are set, an improved particle swarm-ant colony optimization algorithm is used for the solution, and a planning solution strategy considering uncertainty is proposed. Finally, the above conclusions are verified by the actual case data of a park, and the results show that the method proposed in this paper is economical and reasonable.

Vehicle logistics path optimization based on ant colony and particle hybrid algorithm

In the actual vehicle logistics distribution process, the number of vehicles and the choice of distribution path determine the logistics cost. Aiming at the problem of high logistics cost, this paper establishes a multi-objective vehicle logistics path optimization mathematical model considering the distribution process and the number of vehicles. In order to solve the model, a hybrid ant colony particle swarm optimization algorithm is proposed. The algorithm integrates the particle swarm algorithm into the ant colony algorithm, which makes the ant colony algorithm have the characteristics of “particles”, speeds up the search speed of the ant colony algorithm, and reduces the defect of falling into local convergence. The simulation results show that the path optimization model and its hybrid algorithm have fast convergence, can effectively reduce the distribution process and the number of vehicles, and can reduce a large number of vehicle logistics costs, which provides a valuable reference for the whole vehicle logistics path planning.

Study on Conflict-free AGVs Path Planning Strategy for Workshop Material Distribution Systems

The path planning and conflict problem of Multi-AGV Workshop Material Distribution System (MAWMDS) directly determines the efficiency and coordination of system. To solve this problem, a multi-AGV conflict-free path planning model is established, and a collision avoidance factor is designed to guide the AGV to avoid conflicts. An Improved Ant Colony Particle Swarm Optimization Algorithm (IACPSO) is developed, with this algorithm the AGV path planning and node waiting time are optimized at the same time. Finally, a real case is adopted to illustrate that the model and algorithm proposed can effectively avoid road-section and node conflicts.

Association rules mining between service demands and remanufacturing services

AbstractThe potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames

We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively.

Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network

Function approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding responses. In the recent past, the improved neural networks including intuitive, interpretable correlated-contours fuzzy rules for classification tasks were proposed. However, the acquired data set can contain large volume of data and noise that degrades the classification ability of the model and increases the computational time. Thus, it is important to consider this problem which was not focused on recent existing works. Furthermore, there are also some neuron regularization issues in the second layer. To solve this issue in this proposed system Bat optimization based feature selection is proposed for optimal selection of features from the available dataset. Then classification is done by using enhanced neural network including intuitive and interpretable correlated-contours fuzzy rules (EC-FR). According to fuzzy rules extraction, an appropriate framework is built-in which similarity-based directional component of data partitioning and also a model to form cloud data is presented. Neurons weight and bias values are computed by adapting wavelet functions. Finally, parameters of the fuzzy neural networks are fine-tuned using the hybrid ant colony particle swarm optimization (HASO). Performance is evaluated primarily in accordance with the subsequent metrics like precision, recall, accuracy, and error rate.

Neuro Fuzzy System with Hybrid Ant Colony Particle Swarm Optimization (HASO) and Robust Activation

Neuro Fuzzy System with Hybrid Ant Colony Particle Swarm Optimization (HASO) and Robust Activation

Particle Swarm Ant Colony Optimization Algorithm for Solving Circle Permutation Problem

Particle Swarm Ant Colony Optimization Algorithm for Solving Circle Permutation Problem

Swarm Intelligence-based Power Allocation and Relay Selection Algorithm for wireless cooperative network

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

Hybrid Particle Swarm-Ant Colony Algorithm to Describe the Phase Equilibrium of Systems Containing Supercritical Fluids with Ionic Liquids

AbstractBased on biologically inspired algorithms, a thermodynamic model to describe the vapor-liquid equilibrium of binary complex mixtures containing supercritical fluids and ionic liquids, is presented. The Peng-Robinson equation of state with the Wong-Sandler mixing rules are used to evaluate the fugacity coefficient on the systems. Then, a hybrid particle swarm-ant colony optimization was used to minimize the difference between calculated and experimental bubble pressure, and calculate the binary interaction parameters for the excess Gibbs free energy of all systems used. Simulations are carried out in nine systems with imidazolium-based ionic liquids. The results show that the bubble pressures were correlated with low deviations between experimental and calculated values. These deviations show that the proposed hybrid algorithm is the preferable method to describe the phase equilibrium of these complex mixtures, and can be used for other similar systems.

Hybrid meta-heuristic algorithm for job shop scheduling with due date time window and release time

This study intends to solve the job shop scheduling problem with both due data time window and release time. The objective is to minimize the sum of earliness time and tardiness time in order to reduce the storage cost and enhance the customer satisfaction. A novel hybrid meta-heuristic which combines ant colony optimization (ACO) and particle swarm optimization (PSO), called ant colony–particle swarm optimization (ACPSO), is proposed to solve this problem. Computational results indicate that ACPSO performs better than ACO and PSO.

Optimal Design of Single Layer Domes Using Meta-Heuristic Algorithms; a Comparative Study

Domes are lightweight and elegant structures that provide cost-effective solutions to cover large areas. The meta-heuristic algorithms presented in this paper carry out the optimum design of dome structures. The serviceability and strength requirements are considered in the design problem of network, Schwedler and lamella domes as specified in LRFD-AISC. The optimum solutions of the design problem are obtained using particle swarm optimizer, ant colony optimization, harmony search, Big Bang-Big Crunch, heuristic particle swarm ant colony optimization and charged system search. The solutions of these algorithms are presented to demonstrate the effectiveness and the strength of each algorithm. Comparison of the results of the dome designs obtained by these algorithms illustrates the good performance of the heuristic particle swarm, ant colony optimization and charged system search method compared to other heuristic algorithms. Also, the Schwedler dome is found to have the most economical configuration.

An Improved PSO-ACO Algorithm for Solving Large-Scale TSP

In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.

Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures

A heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimization and harmony search scheme. HPSACO applies PSOPC for global optimization and the ant colony approach is used to update positions of particles to attain the feasible solution space. HPSACO handles the problem-specific constraints using a fly-back mechanism, and harmony search scheme deals with variable constraints. Results demonstrate the efficiency and robustness of HPSACO, which performs better than the other PSO-based algorithms having higher converges rate than PSO and PSOPC.

Particle swarm and ant colony algorithms hybridized for improved continuous optimization

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.