Tournament Genetic Algorithm Research Articles

Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints

The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on “gaps” and reducing some PFa regions' sampling too frequently. Such a balancing mechanism allows B-NTGA to be more adaptive and focus on less explored PFa regions. The proposed B-NTGA is investigated on two benchmark multi- and many-objective optimization real-world problems, like Thief Traveling Problem and Multi-Skill Resource-Constrained Project Scheduling Problem. The results of experiments show that B-NTGA has a higher efficiency and better performance than state-of-the-art methods.

Investigation of benchmark dataset for many-objective Multi-Skill Resource Constrained Project Scheduling Problem

The paper presents a redefinition of Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) as a many-objective optimization problem. In effect, it brings the problem closer to real-world applications. Five objectives have been defined: cost, duration, average cash flow, average usage of resources and skill overuse. To summarize MS-RCPSP usage, the paper presents a short survey (years 2015–2021) of multi- and many-objective MS–RCPSP solving methods. Moreover, results of investigation of many-objective MS–RCPSP for classic (greedy algorithm, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III)), single objective (decomposition-based) Differential Evolution And Greedy (DEGR) and state-of-the-art Non-dominated Tournament Genetic Algorithm (NTGA2) have been presented. Additionally, results are analysed using multi-objective quality measures (such as Pareto Front Size (PFS), Purity or Inverted Generational Distance (IGD)) to verify the efficiency of the used methods. The paper answers 4 research questions that investigate 5-objective MS-RCPSP domains, approximations of Pareto Fronts, relations between objectives and efficiency of NTGA2 compared to other methods. Finally, the paper is concluded with a summary and propositions for future work.

A novel genetic algorithm based system for the scheduling of medical treatments

The manual scheduling of medical treatment in a health centre is a complex, time consuming, and error prone task. Furthermore, there is no guarantee a manually generated schedule maximises the operational efficiency of the centre. Scheduling problems have seen extensive research across several domains. The current work presents a novel genetic algorithm for the scheduling of repetitive Transcranial Magnetic Stimulation (rTMS) appointments. The proposed List Scheduling Wildcard Tournament Genetic Algorithm (LSWT-GA) combines an innovative survivor selection policy with heuristic population initialisation. The algorithm aims to optimise the operational efficiency of a medical centre through efficient rTMS appointment scheduling. Additionally, the algorithm has the capacity to consider patient priority. Empirical experiments were conducted to evaluate the performance of the proposed algorithm, using a synthetic data set specifically developed to simulate the medical treatment scheduling problem. The experimental results showed the LSWT-GA algorithm outperforms other algorithms, obtaining the optimal makespan more frequently than a List Scheduling Genetic Algorithm (LS-GA) using traditional survivor selection policies and a standard genetic algorithm using random population initialisation (Random-GA). In addition to the novel genetic algorithm, LSWT-GA, the paper also makes a theoretical contribution by evaluating the run time of the LSWT-GA for makespan minimisation. The proposed algorithm and related findings can be applied directly to the administration systems in medical and healthcare centres and helps improve the deployment of medical resources for better treatment effect.

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-3027-2) contains supplementary material, which is available to authorized users.

A study of interval metric and its application in multi-objective optimization with interval objectives

The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order relations by modifying the existing definitions and also modified different definitions of interval mathematics. Using the definitions of interval order relations and interval metric, the multi-objective optimization problem is converted into single objective optimization problem by different techniques. Then the corresponding problems have been solved by hybrid Tournament Genetic Algorithm with whole arithmetic crossover and double mutation (combination of non-uniform and boundary mutations). To illustrate the methodology, five numerical examples have been solved and the computational results have been compared. Finally, to test the efficiency of the proposed hybrid Tournament Genetic Algorithm, sensitivity analyses have been carried out graphically with respect to genetic algorithm parameters.

Solving a multi-objective interval crew-scheduling problem via Genetic Algorithms

In this paper, a crew-scheduling problem has been formulated considering the daily assignment of a set of crew staff to their round-trips for all the public transports (e.g. train, bus or air bus) that will minimize the waiting times and total service times (including waiting times) of all crews separately. In this approach, the waiting times as well as the service times of crews have been taken as intervals. Here the problem has been formulated as a multi-objective assignment problem using interval arithmetic and the order relations (for the case of pessimistic decision making) that represent the decision maker’s preference between interval times. For this purpose, we have used the existing complete definition of order relations recently developed. Finally, the reformulated problem has been converted to a single objective optimization problem using Global Criterion Method and then solved the same by tournament genetic algorithm. The experimental results of the proposed approach to a crew-scheduling problem have been reported.

An application of tournament genetic algorithm in a marketing oriented economic production lot-size model for deteriorating items

The goal of this research is to discuss an application of tournament genetic algorithm (TGA) for solving an economic production lot-size (EPL) model. In this model, the production rate of the item is finite and it is assumed to be a decision variable. The demand rate is also a deterministic function of selling price and the marketing cost. The selling price per unit item is determined by a mark-up over the unit production cost. The deterioration rate at any instant is a linear increasing/decreasing function of time. Partial backlogging shortages are allowed with a variable rate dependent on the length of the waiting time up to the starting of next production. This model is formulated as a highly non-linear constrained optimization problem. To solve this problem, a TGA with steady-state selection, whole arithmetic crossover and non-uniform mutation has been developed and applied. The model has been illustrated with three numerical examples. Finally, sensitivity analyses have been shown graphically to study the variations of the average profit with respect to the different parameters.

Multiobjective Optimal Fuzzy Logic Controller Driven Active and Hybrid Control Systems for Seismically Excited Nonlinear Buildings

The third generation benchmark control problem for seismically excited nonlinear buildings is an effort to evaluate the developed control strategies in order to apply them in field applications. As the fuzzy logic control systems have been applied effectively in various fields, including vibration control of structures, a multiobjective optimal fuzzy logic control system has been proposed in this paper. Two types of control devices, namely, active and hybrid, driven by a fuzzy logic controller (FLC) have been considered in the present study. Nondimensionalized peak interstory drift ratio and peak floor acceleration have been used as the two objective functions for the multiobjective optimal design problem. A two-branch tournament genetic algorithm has been used to find a set of Pareto-optimal solutions, as the optimization problem is not necessarily continuous or convex. Performance of the FLC driven active and hybrid control systems have been evaluated for all three third generation benchmark problems for seismically excited nonlinear buildings (3-, 9- and 20-story). Acceleration and velocity information of different floors have been used as feedback to the FLC. This approach provides a set of Pareto optimal designs, from which a controller design can be selected for the required performance. The FLC driven active control system performs better than the sample controller given in the benchmark problem. Though the number of sensors and control devices are far less, the performance of the hybrid is close to the active control system.

Multi-Objective Optimization of Communication Satellites with Two-Branch Tournament Genetic Algorithm

In spacecraft design, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-definition process. Consequently, designers usually compromise some mission goals to satisfy only one of the primary design objectives. The conceptual stage of the spacecraft design process is formulated into a multi-objective discrete optimization problem. The use of multi-objective design allows the designer to evaluate different design alternatives across the whole set of design objectives. This work addresses two key design objectives for the spacecraft design process: the minimization of total launch mass and the maximization of spacecraft overall reliability. To predict values for the objective and constraint functions, a satellite design tool, which includes a satellite sizing model and a deterministic reliability model, was built and integrated with a genetic algorithm that employs a two-branch tournament to address the dual objective problem. The multi-objective approach was successful in determining sets of discrete design parameters that would minimize the launch mass as well as maximize the reliability of a geostationary communication satellite, using specified payload requirements. The designs generated by this approach appear to fall into three regions of the tradeoff space between the satellite launch mass and the satellite reliability objectives.

Multiobjective optimal FLC driven hybrid mass damper system for torsionally coupled, seismically excited structures

AbstractIn most of the research work on structural vibration control only two‐dimensional plane structural modelling has been considered, although only a few practical building structures can be modelled as planar structures. Therefore, these methods are not directly applicable to the majority of the practical building structures. This paper discusses the design of a multiobjective optimal fuzzy logic controller (FLC) driven hybrid mass damper (HMD) system for seismically excited torsionally coupled building structures. Floor acceleration and velocity information have been used as feedback to the fuzzy logic controller. A three branch tournament Genetic Algorithm has been used for the multiobjective optimal design of the FLC driven HMD system, where the minimization of the non‐dimensionalized peak displacement, acceleration and rotation of the structure about its vertical axis, have been as the three objective functions. The proposed multiobjective optimal fuzzy logic controller has been verified for an example problem reported in the literature. This HMD system consists of four HMDs arranged in such a way that the system can control the torsional mode of vibration effectively in addition to the flexure modes of vibration. Copyright © 2002 John Wiley & Sons, Ltd.

Multiobjective Optimal Structural Vibration Control Using Fuzzy Logic Control System

Fuzzy logic control systems have been applied as an effective control system in various fields, including vibration control of structures. The advantage of this approach is its inherent robustness and ability to handle nonlinearities and uncertainties in structural behavior and loading. In this paper, the design of a multiobjective optimal fuzzy logic controller (FLC) for seismically excited building structures has been discussed. Acceleration and velocity information have been used as feedback to the FLC. Minimization of the peak acceleration and displacement response of the structure, nondimensionalized by an uncontrolled peak acceleration and displacement response, respectively, have been used as the two objectives. A two-branch tournament genetic algorithm has been used to find a set of Pareto-optimal solutions. The proposed multiobjective optimal FLC has been verified on a benchmark problem, namely, a scaled model of a three-story building with an active mass driver system, and the results have been compared with the results from other control methods reported in the literature. It has been shown that the use of acceleration information, in addition to velocity as a feedback for the FLC, results in better structural performance.

Using the Two-Branch Tournament Genetic Algorithm for Multiobjective Design

The two-branch tournament genetic algorithm is presented as an approach to determine a set of Pareto-optimal solutions to multiobjective design problems. Because the genetic algorithm searches using a population of points rather than using a point-to-point search, it is possible to generate a large numher of solutions to multiobjective problems in a single run of the algorithm. The two-branch tournament and its implementation in a genetic algorithm (GA) to provide these solutions are discussed. This approach differs from most traditional methods for GA-based multiobjective design ; it does not require the nondominated ranking approach nor does it require additional fitness manipulations. A multiobjective mathematical benchmark problem and a 10-bar truss problem were solved to illustrate how this approach works for typical multiobjective problems. These problems also allowed comparison to published solutions. The two-branch GA was also applied to a problem combining discrete and continuous variables to illustrate an additional advantage of this approach for multiobjective design problems. Results of all three problems were compared to those of single-objective approaches providing a measure of how closely the Pareto-optimal set is estimated by the two-branch GA. Finally, conclusions were made about the benefits and potential for improvement of this approach.

Using the two-branch tournament genetic algorithm for multiobjective design

Using the two-branch tournament genetic algorithm for multiobjective design