Sub-population Genetic Algorithm Research Articles

BreatheBand: A Fine-grained and Robust Respiration Monitor System Using WiFi Signals

Respiration is a vital indicator of the state of the human body. Monitoring human respiration enables the realization of a variety of intelligent applications, including smart medical and sleep monitoring. Traditional methods that are dependent upon wearable devices are more costly and inconvenient for users. Recent studies have evidenced that low-cost commodity WiFi devices can be used to accomplish contactless respiration monitoring. In this article, we present BreatheBand, a fine-grained and robust respiration monitoring system based on commercial WiFi signals. We first remove the time-varying phase shift in the channel state information (CSI) by developing the Multi-antenna CSI–Subpopulation Genetic algorithm. Then we separate human respiratory components from WiFi signals by employing subcarrier selection and Independent Component Analysis. Next, applying a Mixed Cluster Gaussian–Hidden Markov Model, we generate a respiration signal resembling that of wearable devices. Finally, we integrate the BreatheBand system into commercial WiFi infrastructure. The results show that the BreatheBand’s respiration signal is remarkably identical to the signal collected by the wearable device in various scenarios. In particular, the mean absolute error of the BreatheBand’s respiration rate is approximately 0.1 bpm, outperforming state-of-the-art algorithms.

Optimization of agricultural product storage using real-coded genetic algorithm based on sub-population determination

<span lang="EN-US">The storage of fresh agricultural products is a combinatorial problem that should be solved to to maximize number of items in the storage and also maximize the total profit without exceed the capacity of storage. The problem can be addressed as a knapsack problem that can be classified as NP-hard problem. We propose a genetic algorithm (GA) based on sub-population determination to address the problem. Sub-population GA can naturally divide the population into a set of sub-population with certain mechanism in order to obtain a better result. GA based on sub-population is applied by generating a set of sub-population which is happened in the process of initializing population. A special migration mechanism is developed to maintain population diversity. The experiment shows GA based on sub-population determination provide better results comparable to those achieved by classical GA.</span>

Benders decomposition-based particle swarm optimization for competitive supply networks with a sustainable multi-agent platform and virtual alliances

The involvement of competition in supply networks has changed the existing monopoly platform in different fields. This paper examines a multi-level decision-making framework within a triple-stage strategic approach in competitive supply networks. The various levels of these supply networks consist of parent firms (parent brands), manufacturing plants, state-owned logistics company and franchised sales centers. The parent brands, while following the strategies of the state logistics company (as the leader of the game), seek to further expand their market share in the production, supply and sales sectors. The main contributions of the proposed approach are: the existence of partnership and non-partnership synergies in different stages of planning, the emergence and development of supply networks based on downstream alliances, the design of a multi-agent distribution mechanism based on environmental sustainability requirements, and the simultaneous development of cooperation and competition in terms of virtual alliances. Further, given the features of the issue under discussion, a hybrid Benders Decomposition-Particle Swarm Optimization algorithm is utilized. The designed structure of the algorithm helps to facilitate high-dimensional problem-solving while also addressing the interactive requirements of competitive games. The results of comparing the proposed solution approach with a game-theoretical heuristic, pure Benders decomposition and bi-level sub-population genetic algorithm prove its better performance, especially in large-size instances.

Location and transportation of intermodal hazmat considering equipment capacity and congestion impact: elastic method and sub-population genetic algorithm

Location and transportation of intermodal hazmat considering equipment capacity and congestion impact: elastic method and sub-population genetic algorithm

A fuzzy bi-objective flexible cell scheduling optimization model under green and energy-efficient strategy using Pareto-based algorithms: SATPSPGA, SANRGA, and NSGA-II

A bi-objective flexible cell scheduling problem (CSP) under time-of-use (TOU) electricity tariffs in both deterministic and fuzzy environments are developed with the following objectives: (1) total cost of production system and (2) total delivery tardiness of jobs. To create a form of green and energy-conscious strategy, cost of produced emission and consumed power and some limitations on entire energy consumption, total manufactured emission, setup time, part defect (pert) percentage, system productivity, total number of tool slots, operations, and automated guided vehicles (AGVs) are respected. Besides, a self-adaptive two-phase subpopulation genetic algorithm (SATPSPGA) is taken to find a near-optimum solution of suggested bi-objective fuzzy mixed integer linear programming (FMILP) model. In view of the fact that no benchmark is existing in the literature, a self-adaptive non-dominated ranked genetic algorithm (SANRGA) and a non-dominated sorting genetic algorithm (NSGA-II) were employed to solve the model. To validate the result, a GAMS model of problem is utilized and compared with algorithm outcomes as well. Lastly, several numerical examples are offered to show the utility of the proposed methodology.

A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution

The learning effects of the workers and machine deterioration in an aggregate production planning (APP) problem have not been taken into account in the literature yet. These factors affect the performance of any real-world production system and require attention. In this paper, a bi-objective optimization model is developed for an APP problem with labor learning effect and machine deterioration. The first objective of this model maximizes the profit by improving learning and reducing the failure cost of the system. The second objective function minimizes the costs associated with repairs and deterioration, which depend on the failure rate of the machines in the production periods. The aim of this article is to obtain appropriate levels of production rates in regular and overtimes, inventory and shortage levels, workers' hiring and firing levels, and the quantities of the products that are subcontracted. To demonstrate the validity of the proposed mathematical formulation, the multi-objective model is converted into a single-objective model using the fuzzy goal programming method, based on which computational experiments are performed on a set of random small-sized instances solved by the LINGO software. As the problem is shown NP-hard, a subpopulation genetic algorithm (SPGA) is proposed to solve large-size problems. In addition, two other meta-heuristics called weighted sum multi-objective genetic algorithm (WMOGA) and non-dominated sorting genetic algorithm II (NSGA-II) are utilized to solve a set of benchmark problems, in order to validate the results obtained and to assess the performance of the SPGA. For tuning the parameters, the Taguchi method is proposed in order to obtain high-quality solutions. Finally, the performances of the proposed algorithms are statistically compared together. The computational results show that SPGA compared to the other algorithms has a better performance in terms of some multi-objective optimization criteria.

Three self-adaptive multi-objective evolutionary algorithms for a triple-objective project scheduling problem

Finding a Pareto-optimal frontier is widely favorable among researchers to model existing conflict objectives in an optimization problem. Project scheduling is a well-known problem in which investigating a combination of goals eventuate in a more real situation. Although there are many different types of objectives based on the situation on hand, three basic objectives are the most common in the literature of the project scheduling problem. These objectives are: (i) the minimization of the makespan, (ii) the minimization of the total cost associated with the resources, and (iii) the minimization of the variability in resources usage. In this paper, three genetic-based algorithms are proposed for approximating the Pareto-optimal frontier in project scheduling problem where the above three objectives are simultaneously considered. For the above problem, three self-adaptive genetic algorithms, namely (i) A two-stage multi-population genetic algorithm (MPGA), (ii) a two-phase subpopulation genetic algorithm (TPSPGA), and (iii) a non-dominated ranked genetic algorithm (NRGA) are developed. The algorithms are tested using a set of instances built from benchmark instances existing in the literature. The performances of the algorithms are evaluated using five performance metrics proposed in the literature. Finally according to the technique for order preference by similarity to ideal solution (TOPSIS) the self-adaptive NRGA gained the highest preference rank, followed by the self-adaptive TPSPGA and MPGA, respectively.

Using a meta-heuristic algorithm for solving the multi-mode resource-constrained project scheduling problem

Resource-constrained project scheduling problem (RCPSP) is one of the most important problems in the context of project scheduling. It consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimised. Robustness is an objective that recently has been noticed. Robustness of the scheduling means that if some activities take more time than the estimated of the scheduling phase, project finish date will not change without any cost. In this paper, a bi-objective single mode RCPSP is expanded to the multi-mode resource-constrained project scheduling problem (MRCPSP) with robustness and makespan criteria. Taking these two objectives in the MRCPSP as a bi-objective problem and using metaheuristic algorithms to solve it has not been studied until now. A new bi-objective mathematical model for the problem is presented. The problem formed in this way is NP-hard, so we apply an evolutionary algorithm named sub-population genetic algorithm (SPGA) to find Pareto solutions. Standard sets of instances from the project scheduling problem library (PSPLIB) are used and solved by the proposed algorithms to show the efficiency of our algorithm to solve the problem.

Bi-objective hybrid flow shop scheduling with sequence-dependent setup times and limited buffers

The bi-objective hybrid flow shop problem with sequence-dependent setup times and limited buffers is mentioned in this paper. In this environment, there are limited buffer spaces between any two successive stages; thus, maybe there is not enough room for queues of jobs that are waiting in the system for their next operations. This problem is shown to be NP-hard in the strong sense. Up to now, some heuristic and metaheuristic approaches are proposed to minimize makespan or total tardiness of jobs. This paper presents several methods for optimization which consider two objectives simultaneously. The resolution of several specific instances from the open literature with the adaptations of non-dominated sorting genetic algorithm and sub-population genetic algorithm suggest that the proposed algorithms are effective and useful methods for solving this problem.

Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.

Minimizing the makespan and the system unavailability in parallel machine scheduling problem: a similarity-based genetic algorithm

This paper is devoted to ponder a joint production and maintenance problem. For solving the problem, a genetic algorithm named similarity-based subpopulation genetic algorithm (SBSPGA) is introduced. SBSPGA is presented based on a well-known evolutionary algorithm, the subpopulation genetic algorithm II (SPGA-II). Compared with the SPGA-II, the innovation of the SBSPGA could be divided into two parts: (1) using a similarity model for the elitism strategy and (2) performing the algorithm in just one stage. To tackle the maintenance aspect, reliability models are employed in this paper. The aim of this paper was to optimize two objectives: minimization of the makespan for the production part and minimization of the system unavailability for the maintenance part. To execute our proposed problem, two decisions must be made at the same time: achieving the best assignment of n jobs on m machines to minimize the makespan and determining the time at which the preventive maintenance activities must be performed to minimize the system unavailability. The maintenance activity numbers and the maintenance intervals are not fixed in advanced. Promising the acquired results, a benchmark with tremendous number of test instances (more than 5,000) is employed.

A multi-phase covering Pareto-optimal front method to multi-objective parallel machine scheduling

This paper considers the problem of parallel machine scheduling with sequence-dependent setup times to minimise both makespan and total earliness/tardiness in the due window. To tackle the problem considered, a multi-phase algorithm is proposed. The goal of the initial phase is to obtain a good approximation of the Pareto-front. In the second phase, to improve the Pareto-front, non-dominated solutions are unified to constitute a big population. In this phase, based on the local search in the Pareto space concept, three multi-objective hybrid metaheuristics are proposed. Covering the whole set of Pareto-optimal solutions is a desired task of multi-objective optimisation methods. So in the third phase, a new method using an e-constraint hybrid metaheuristic is proposed to cover the gaps between the non-dominated solutions and improve the Pareto-front. Appropriate combinations of multi-objective methods in various phases are considered to improve the total performance. The multi-phase algorithm iterates over a genetic algorithm in the first phase and three hybrid metaheuristics in the second and third phases. Experiments on the test problems with different structures show that the multi-phase method is a better tool to approximate the efficient set than the global archive sub-population genetic algorithm presented previously.

A multi-phase covering Pareto-optimal front method to multi-objective scheduling in a realistic hybrid flowshop using a hybrid metaheuristic

This paper considers the problem of sequence-dependent setup time hybrid flowshop scheduling with the objectives of minimizing the makespan and sum of the earliness and tardiness of jobs, and present a multi-phase method. In initial phase, the population will be decomposed into several subpopulations. In this phase we develop a random key genetic algorithm and the goal is to obtain a good approximation of the Pareto-front. In the second phase, for improvement the Pareto-front, non-dominant solutions will be unified as one big population. In this phase, based on the local search in Pareto space concept, we propose multi-objective hybrid metaheuristic. Finally in phase 3, we propose a novel method using e-constraint covering hybrid metaheuristic to cover the gaps between the non-dominated solutions and improve Pareto-front. Generally in three phases, we consider appropriate combinations of multi-objective methods to improve the total performance. The hybrid algorithm used in phases 2 and 3 combines elements from both simulated annealing and a variable neighborhood search. The aim of using a hybrid metaheuristic is to raise the level of generality so as to be able to apply the same solution method to several problems. Furthermore, in this study to evaluate non-dominated solution sets, we suggest several new approaches. The non-dominated sets obtained from each phase and global archive sub-population genetic algorithm presented previously in the literature are compared. The results obtained from the computational study have shown that the multi-phase algorithm is a viable and effective approach.

The development of a sub-population genetic algorithm II (SPGA II) for multi-objective combinatorial problems

Previous research has shown that sub-population genetic algorithm is effective in solving the multi-objective combinatorial problems. Based on these pioneering efforts, this paper extends the SPGA algorithm with a global Pareto archive technique and a two-stage approach to solve the multi-objective problems. In the first stage, the areas next to the two single objectives are searched and solutions explored around these two extreme areas are reserved in the global archive for later evolutions. Then, in the second stage, larger searching areas except the middle area are further extended to explore the solution space in finding the near-optimal frontiers. Through extensive experimental results, SPGA II does outperform SPGA, NSGA II, and SPEA 2 in the parallel scheduling problems and knapsack problems; it shows that the approach improves the sub-population genetic algorithm significantly. It may be of interests for researchers in solving multi-objective combinatorial problems.

Sub-population genetic algorithm with mining gene structures for multiobjective flowshop scheduling problems

According to previous research of Chang et al. [Chang, P. C., Chen, S. H., & Lin, K. L. (2005b). Two phase sub-population genetic algorithm for parallel machine scheduling problem. Expert Systems with Applications, 29(3), 705-712], the sub-population genetic algorithm (SPGA) is effective in solving multiobjective scheduling problems. Based on the pioneer efforts, this research proposes a mining gene structure technique integrated with the SPGA. The mining problem of elite chromosomes is formulated as a linear assignment problem and a greedy heuristic using threshold to eliminate redundant information. As a result, artificial chromosomes are created according to this gene mining procedure and these artificial chromosomes will be reintroduced into the evolution process to improve the efficiency and solution quality of the procedure. In addition, to further increase the quality of the artificial chromosome, a dynamic threshold procedure is developed and the flowshop scheduling problems are applied as a benchmark problem for testing the developed algorithm. Extensive tests in the flow-shop scheduling problem show that the proposed approach can improve the performance of SPGA significantly.

Two‐phase sub population genetic algorithm for parallel machine-scheduling problem

This paper introduces a two‐phase sub population genetic algorithm to solve the parallel machine-scheduling problem. In the first phase, the population will be decomposed into many sub-populations and each sub-population is designed for a scalar multi-objective. Sub-population is a new approach for solving multi-objective problems by fixing each sub-population for a pre-determined criterion. In the second phase, non-dominant solutions will be combined after the first phase and all sub-population will be unified as one big population. Not only the algorithm merges sub-populations but the external memory of Pareto solution is also merged and updated. Then, one unified population with each chromosome search for a specific weighted objective during the next evolution process. The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy. Experimental results are reported and the superiority of this approach is discussed.