Research on the performance of multi-population genetic algorithms with different complex network structures

In this investigation two new modifications of standard multi-population genetic algorithm have been developed. Modifications differ from each other in the sequence of execution of main genetic operators selection, crossover and mutation. The main idea of both modifications is the operator selection to be executed between the operators crossover and mutation, or between mutation and crossover, respectively. Newly elaborated modifications of multi-population genetic algorithm s together with the standard one have been investigated for a parameter identification of yeast fed-batch cultivation . Obtained results have been compared and newly proposed modifications have been shown as accurate as the standard one multi-population genetic algorithms even proved to be faster . Normal 0 21 false false false BG X-NONE X-NONE

Quantifying the importance of specific types of higher-order structures in complex networks

The aerosol number size distribution is the main parameter for characterizing aerosol optical properties and physical properties, it has a major influence on radiation forcing. With regard to some disadvantages in the traditional methods, a method based on the multi population genetic algorithm (MPGA) is proposed and employed to retrieve the aerosol size distribution of dust particles. The MPGA principles and design are presented in detail. The MPGA has better performance compared with conventional methods. In order to verify the feasibility of the inversion method, the measured aerosol optical thickness (AOT) data of dust particles taken by a sun photometer are used and a series of comparisons between the simple genetic algorithm (SGA) and MPGA are carried out. The results show that the MPGA presents better properties when compared with the SGA with smaller inversion errors, smaller population size and fewer generation numbers to retrieve the aerosol size distribution. The MPGA inversion method is analyzed using the background day, dust storm event and seasonal size distribution. The method proposed in this study has important applications and reference value for aerosol particle size distribution inversion.

Multi Population Genetic Algorithm to estimate snow properties from GPR data

To solve the problem of large number of redundant search existing in traditional genetic algorithm for solving Flexible Job Shop Scheduling Problem (FJSP), a two-layer genetic algorithm based on matrix coding for FJSP was proposed. The algorithm used double matrix structure to encode which included machine selection matrix and operation sorting matrix. Outer genetic algorithm is used to search for the best option of machine selection, which transform FJSP to JSP. Inner genetic algorithm is used to solve the sub-problem of JSP based on machine selection matrix. The effectiveness of proposed algorithm was verified by analyzing the results of benchmarks.

A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines

Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.

Production scheduling is a critical requirement in today’s production systems. The production scheduling problem focuses on the rational allocation of production resources, determining the processing order of production tasks and operations, satisfying production constraints arising from the production environment such as the number of machines and task duration, and achieving optimal production goals, such as completing all tasks in the fastest way possible. The Flexible Job Shop Scheduling (FJSP) problem is a classical production scheduling issue, an extension of the Job Shop Scheduling (JSP) problem. The JSP problem involves allocating a set of jobs to a set of machines, where each job consists of multiple operations that must adhere to a precedence order. Each machine can process only one operation at a time for each job, and the time required for each operation on the corresponding machine is predetermined. The challenge in the JSP problem is to determine an appropriate order for processing operations or assignments, based on the production objective. FJSP represents a more complex and flexible variant of this problem, where at least one operation of a task can be performed on a set of alternative machines. Given that FJSP problems are NP-hard, solutions tend to be complex, varied, and often have limited applicability. Many FJSP scenarios require targeted modeling. In this paper, I propose a modeling approach for the static scheduling problem of job shops, considering low flexibility and demonstrating its usability by comparing it with other scheduling approaches, such as those based on genetic algorithm modeling. The paper is organized as follows:The first part of this paper offers a brief introduction to the FJSP problem, including background, research motivation, and main contributions, the second part provides a literature review,including a brief review and introduction of recent solutions to the FJSP problem, the third part describes a specific modeling approach, the fourth part details the test experiments and results of the modeling approach and includes comparisons with some other approaches, the fifth part analyzes the results from the fourth part and discusses the advantages and disadvantages of the modeling approach relative to others, and the sixth part concludes with a summary of findings and recommendations.

Flexible job-shop scheduling problem (FJSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving the FJSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement. In this paper, we present a genetic algorithm (GA) for FJSP. The algorithm encodes the individual with parallel machine process sequence based code, integrates the Most Work Remaining, the Most Operation Remaining and random selection strategies for generating the initial population, and integrates the binary tournament selection and the linear ranking selection strategies to reproduce new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results than the traditional genetic algorithms. The integrated genetic algorithm is effective for solving FJSP.

The dual-resource constrained flexible job shop scheduling problem with variable sublots (DRCFJSP-VS) can be decomposed into four subproblems: the sublot splitting subproblem, the sublot sequencing subproblem, the machine assignment subproblem, and the worker assignment subproblem, which are difficult to solve efficiently using conventional methods. The introduction of variable-size batch splitting and the constraints of multiple levels and skills of workers further increase the complexity of the problem, making it difficult to solve efficiently using conventional methods. This paper proposes a mixed-integer linear programming (MILP) model to solve this complex problem and introduces a two-stage multi-objective evolutionary algorithm (TSMOEA). In the first stage of the algorithm, an improved multi-objective discrete difference evolutionary algorithm is used to optimize the dual-resource constrained flexible job shop scheduling problem; in the second stage, an adaptive simulated annealing algorithm is used to search for variable-size batch splitting strategies. To validate the feasibility of the model, the solution results are obtained using the CPLEX solver and compared with the results of TSMOEA. The performance of TSMOEA is compared with NSGA-II, PSO, DGWO, and WOA on improved instances. The results show that TSMOEA outperforms the other algorithms in both IGD and HV metrics, demonstrating its superior solution quality and robustness.

Orthogonal Frequency Division Multiplexing(OFDM) is a multi-carrier transmission with strong anti-fading, efficient spectrum utilization and data transfer rates. It is considered to be one of the core technologies of 4G mobile communication system. This paper firstly analyzes the partial transmit sequences(PTS) PAPR reduction algorithm which is widely studied in the OFDM system PAPR reduction algorithms in recent years, and mainly researches the improved PTS algorithm, namely the genetic PTS algorithm(GA-PTS). And this paper proposes an multi-population genetic PTS algorithm based on the genetic PTS algorithm. The multi-population genetic PTS algorithm firstly brings in migration operator and artificial selection operator, and then uses Multiple populations to search optimal factors of PTS at the same time, therefore it reduces the computing complexity of the algorithm. The simulation experiments show that compared with the traditional PTS algorithm, the improved multi-population genetic PTS algorithm reduces the complexity without causing significant PAPR performance losses.

Large scale batch scheduling problems with complex constraints are difficult and time-consuming to solve. Therefore, this paper addresses the green batch scheduling problem on non-identical parallel machines with time-of-use electricity prices. The objective of the problem is to minimise total electricity costs (TEC) in production. Two kinds of algorithms—single-population genetic algorithms (SPGA) and multi-population genetic algorithm (MPGA)—are proposed to solve the problem. In the algorithms, the products are allocated into batches and are then allocated to machines randomly. A greedy strategy is designed to arrange the production sequence and the starting time of the batches. Furthermore, a self-adaptive parameter adjustment strategy is proposed to enhance the adaptability of the algorithm. Computational experiments with CPLEX solver have been conducted to evaluate the performance of the algorithms. On small instances, both SPGA and MPGA can achieve approximate results compared with those obtained by CPLEX, and can also achieve smaller TEC on large instances with less computing time. In addition, the proposed MPGA implemented by parallel computing outperforms SPGA in getting better results with nearly the same computing time.

Scheduling is to determine when and who to process the task in order to minimize the makespan, the cost, and the tardiness/earliness of jobs, etc. Evolutionary algorithms (EAs) are very promising approaches for the scheduling problems due to its dynamic characteristics, multiple contradicting objectives and highly nonlinear constraints. Brain storm optimization (BSO) algorithm and its variations are new emerging evolutionary algorithms. In this chapter, our focus will be on BSOs for the flexible job shop scheduling problems (FJSP). First, FJSP will be formulated as a combinatorial optimization problem. Second, approaches for FJSP will be summarized briefly. Third, BSOs will be introduced and the key issues in the application of BSOs for FJSP will be emphasized. Fourth, four BSOs will be developed to solve FJSP and the details designed for FJSP will be illustrated. Their strength and weakness will be discussed as well. Finally, a group of experiments will be conducted to compare the four BSO algorithms. The results will be analyzed statistically. Finally, a conclusion will be summarized.

In recent years, multi-population genetic algorithms (MGAs) have been recognized as being more effective both in speed and solution quality than single-population genetic algorithms (SGAs). Despite of these advantages, the behavior and performance of MGAs, like SGAs, are still heavily affected by a judicious choice of parameters, such as connection topology, migration method, migration interval, migration rate, population number, etc. In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration interval as well as migration rate on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to evolve the appropriate migration interval and migration rate for MGAs. Experiments on the 0/1 knapsack problem showed that our approach can compete with MGAs with static migration parameters.

Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these techniques. Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines. In this paper, we consider the FJSP with parallel operations (EFJSP) and we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the problem. Several FJSP and EFJSP instances were used to evaluate the performance of the proposed algorithms. Comparisons among our methods and state-of-the-art algorithms are also provided. The experimental results demonstrate that the FA and GA achieved improvements in terms of efficiency and efficacy. Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search. In addition, based on our initial experiments, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than our proposed genetic algorithm for the considered problem.