A gamma type-2 defuzzification method for solving a solid transportation problem considering carbon emission

Transportation policy seeks to improve agency freight and cargo management and enhance sustainable, efficient and effective transportation operations. In this paper, four new fuzzy fixed charge solid transportation problems (FFCSTP) are formulated to maximise the total profit and minimise the total cost. The interval objective function is approximated to an intervalvalued function, i.e., transformed to a single objective using weighted sum method and weighted multiplication method. The fuzzy constraints are converted to its equivalent deterministic form using different interval order relations. Genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are used to obtain the optimal transportation schedule for the proposed solid transportation problem. During the evaluation of the models, in one case, limitation on the transported amounts is imposed and in other case, no such limitation is used. The models are illustrated with numerical examples and the optimum results of the models are compared.

Transportation policy seeks to improve agency freight and cargo management and enhance sustainable, efficient and effective transportation operations. In this paper, four new fuzzy fixed charge solid transportation problems (FFCSTP) are formulated to maximise the total profit and minimise the total cost. The interval objective function is approximated to an intervalvalued function, i.e., transformed to a single objective using weighted sum method and weighted multiplication method. The fuzzy constraints are converted to its equivalent deterministic form using different interval order relations. Genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are used to obtain the optimal transportation schedule for the proposed solid transportation problem. During the evaluation of the models, in one case, limitation on the transported amounts is imposed and in other case, no such limitation is used. The models are illustrated with numerical examples and the optimum results of the models are compared.

In this paper the authors investigate the application of the particle swarm optimization (PSO) technique for solving the hardware/software partitioning problem. The PSO is attractive for the hardware/software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. The authors carried out several tests on a hypothetical, relatively-large hardware/software partitioning problem using the PSO algorithm as well as the genetic algorithm (GA), which is another evolutionary technique. The authors found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, the authors tested several hybrid combinations of PSO and GA algorithm; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. The paper proposes to name this successive PSO algorithm as the re-excited PSO algorithm

In this paper we investigate the application of the Particle Swarm Optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. We carried out several tests on a hypothetical, relatively-large Hardware/Software partitioning problem using the PSO algorithm as well as the Genetic Algorithm (GA), which is another evolutionary technique. We found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, we tested several hybrid combinations of PSO and GA algorithms; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. We propose to name this successive PSO algorithm as the Re-excited PSO algorithm. The constrained formulations of the problem are investigated for different tuning or limiting design parameters constraints.

Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.

In order to improve the safety level, airspace capacity, operation efficiency and service ability of civil aviation, particle swarm optimization (PSO), genetic algorithm (GA) and hybrid genetic particle swarm optimization are simulated and applied in ADS-B ground station selection. The algorithm flow, advantages and disadvantages of PSO and GA are analyzed. In order to make up for the limitations of the above algorithms, the hybrid genetic particle swarm optimization algorithm is proposed for ADS-B site selection. Through virtual site selection, GA, PSO and hybrid genetic particle swarm optimization algorithm are respectively used for simulation. Comparing with the results of site selection, it is verified that genetic particle swarm optimization algorithm is more suitable for the site selection of ground station.

Extended Abstract Introduction Site selection for health centers and hospitals in proper locations and the allocation of population to them is an important issue in urban planning. The location and allocation of health and medical facilities including hospitals, have long been an important issue for urban planners that has become more complicated with the growth of population. Location and allocation of hospitals is basically planned to ensure the availability of proper and comprehensive health services as well as the reduction of the establishment costs. Improper planning of the health centers has created multiple problems for big cities in developing countries in recent years. In the present study, the Genetic Algorithm (GA), Hybrid Particle Swarm Optimization algorithm (HPSO), Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used for selecting proper sites of hospital and allocating the demanded locations to these centers in District 2 of Tehran. Materials & Methods The main goal of this research is to compare and evaluate the performance of the Genetic Algorithm (GA) and Hybrid Particle Swarm Optimization algorithm (HPSO) for determining the optimal locations of hospital centers and allocating the population blocks to them. In order to limit the search space, the analyzing capabilities of the Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used to select the candidate sites satisfying the initial conditions and criteria. The locations of such candidate centers are the input of the optimization section. The accuracy of the entire process strongly depends on the selection of these candidate sites. Hence, in this paper, the Analytic Hierarchy Process (AHP) method has been used to select the candidate centers. Then, two optimization algorithms were applied in choosing six optimum sites from the candidate locations and allocating the population to them through minimizing the overall distances between the centers and their allocated blocks. In this study, to improve the Particle Swarm Optimization, a simple neighborhood search has been proposed for better exploitation of the elite particles. The main purpose of this neighborhood search is to increase the convergence rate of the algorithm without decreasing the random search. Since the neighborhood search has a specific definition proportional to each issue, and the issues of location and allocation are spatial issues as well, therefore, the geographic principle of appropriate distribution of the centers in space has been used to define the neighborhood search (the distance between the centers should not be less than a certain amount). In an elite particle, two centers with the lowest distance are selected and one of them is replaced by a new and randomly selected center. If such a change provides a better objective function, the newly created solution in the elite particle is replaced. To calibrate the algorithms parameters, a simulated data set has been used. Having proper values for those parameters, the algorithms were tested on the real data of the study area. Results & Discussion Given the results of algorithms on real data, the performances of both algorithms are highly dependent on the initial population and the allowed number of iterations. In general, lower numbers of iterations and more populations brings better results than the higher iterations and lower populations. The results show that the Hybrid Particle Swarm Optimization (HPSO) has better performance than the Genetic Algorithm (GA). The convergence rate of the Hybrid Particle Swarm Optimization (HPSO) algorithm is faster than the genetic algorithm (GA), which can be attributed to the particle’s motion toward the best personal and global experiences. Furthermore, the proposed neighborhood search has caused the HPSO algorithm to converge earlier. To evaluate the repeatability of the algorithms, they were performed 40 times for both simulated and real data. Both algorithms have displayed high levels of repeatability, but the Hybrid Particle Swarm Optimization (HPSO) algorithm is more stable. However, the use of Genetic Algorithm (GA) on simulated data has shown more stability than its use on real data. For both the simulated data and real data, the Hybrid Particle Swarm Optimization (HPSO) algorithm performs faster than the Genetic Algorithm (GA). Conclusion Simplicity and repeatability of the algorithm are among the important factors which are very significant from the user’s point of view. In this research, the HPSO algorithm has not only been repeatable and simple, but has performed faster than the GA. Therefore, considering these criteria, regarding the special case of this research, the HPSO seems to be more promising than the GA.

Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, this paper proposes a weighted sum optimization method using a comprehensive evaluation model to address TTDP, a complex multi-objective optimization problem. The goal of the research is to balance experience, cost, and efficiency by using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to assign subjective and objective weights to indicators such as ratings, duration, and costs. These weights are optimized using the Lagrange multiplier method and integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. Additionally, a weighted sum optimization method within the Traveling Salesman Problem (TSP) framework is used to maximize ratings while minimizing costs and distances. Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. The study then introduces the Lagrange multiplier method to the algorithms, improving the solution quality of all seven heuristic algorithms, with an average solution quality improvement of 112.5% (from 0.16 to 0.34). The PSO algorithm achieves the best solution quality. Based on this, the study introduces a new variant of PSO, namely PSO with Laplace disturbance (PSO-LD), which incorporates a dynamic adaptive Laplace perturbation term to enhance global search capabilities, improving stability and convergence speed. The experimental results show that PSO-LD outperforms the baseline PSO and other algorithms, achieving higher solution quality and faster convergence speed. The Wilcoxon signed-rank test confirms significant statistical differences among the algorithms. This study provides an effective method for experience-oriented path optimization and offers insights into algorithm selection for complex TTDP problems.

Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks.

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings – Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm. Originality/value – The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

A TDMA scheduling scheme for many-to-one communications in wireless sensor networks

This paper proposes application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the design of direct-driven permanent magnet synchronous generator machine (PMSGs) for wind turbine applications. The power rating of these machines is in the mega watt (MW) level. The constraints and requirements of the generator are outlined. The proposed design scheme optimizes various PMSG parameters like Pole pair number, Linear current density, Air gap thickness, Rotor outer diameter, Relative width of the permanent magnet etc to achieve certain objectives like maximizing efficiency, increasing Torque, improving power factor etc. The results obtained by GA algorithm and those by PSO algorithm are compared. The performance of Particle Swarm Optimization is found to be better than the Genetic Algorithm, as the PSO carries out global search and local searches simultaneously, whereas the Genetic Algorithm concentrates mainly on the global search. Results show that the proposed PSO optimization algorithm is easy to develop and apply and produced competitive designs compared to the GA algorithm.

Optimal operation of trunk natural gas pipelines via an inertia-adaptive particle swarm optimization algorithm

A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem—the geometry of diesel engine combustion chamber reducing exhaust emissions such as NOx, soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate that the volume near the lower central part of the combustion chamber has a large effect on exhaust emissions and the optimum chamber geometry will vary depending on fuel injection angle.