Hybrid Population-based Algorithm Research Articles

A hybrid population-based algorithm for solving the Minimum Dominating Set Problem

The Minimum Dominating Sets (MDS) problem is pivotal across diverse fields like social networks and ad hoc wireless networks, representing a significant NP-complete challenge in graph theory. To tackle this, we propose a novel hybrid cuckoo search approach. While cuckoo search is renowned for its wide search space exploration, we enhance it with intensification methods and genetic crossover operators for improved performance. Comprehensive experimental evaluations against state-of-the-art techniques validate our approach's effectiveness.

Optimization of Truss Structures by Using a Hybrid Population-Based Metaheuristic Algorithm

Optimization of Truss Structures by Using a Hybrid Population-Based Metaheuristic Algorithm

A hybrid population-based algorithm for solving the fuzzy capacitated maximal covering location problem

In this paper, we study the fuzzy capacitated maximal covering location problem, where a hybrid population-based algorithm is proposed to solve it. An instance of the problem is represented by a set of customers in a given network with their distances and such that the coverage degree of facilities and the distance between customers are fuzzy. Its goal is to open (select) a subset of facilities to be located on customers such that the coverage of customers should be maximized. Two versions of a hybrid population-based algorithm are designed for tackling the aforementioned problem: (i) a modified grey wolf optimizer combined with exploration and exploitation strategies, and (ii) a modified grey wolf optimizer combined with a series of local searches and an upper bound delimiting wolf movements. Each version of the method enhances the quality of the solutions belonging to the current population by adding a series of local searches. Further, the exploration strategy is based upon the drop/rebuild operator for jumping from the current region to unvisited ones. Finally, the behavior of the proposed method is evaluated on a set of benchmark instances of the literature, where its provided results are compared to those reached by more recent methods of the literature and the state-of-the-art Cplex solver. Encouraging results have been obtained.

A hybrid population-based algorithm for the bi-objective quadratic multiple knapsack problem

In this paper, the bi-objective quadratic multiple knapsack problem is tackled with a hybrid population-based method. The proposed method starts by computing two reference solutions, where a specialized powerful mono-objective algorithm is used. From both reference solutions, a starting population is built by using a series of perturbations around the solutions. Next, the so-called non-sorting genetic process is combined with a new drop/rebuild operator for generating a series of populations till converging toward an approximate Pareto front with high density. The performance of the hybrid population based algorithm (namely HBPA) is evaluated on a set of benchmark instances of the literature containing both medium and large-scale instances. Its provided results are compared to those achieved by the best methods available in the literature. Encouraging results have been obtained.

A population-based algorithm with the selection of evaluation precision and size of the population

In this paper, a new nature-inspired hybrid population-based algorithm is proposed. Firstly, during its operation, it changes the size of the population to reduce the number of processed individuals. For this purpose, dedicated functions that determine the size of population for each algorithm step are used. Secondly, for each individual of the population, the algorithm selects and changes an operator for its modification. This provides a balance between searching for new solutions and fine-tuning of those already found. Thirdly, the algorithm can control the sampling period of the optimized (dynamic) systems, reducing the complexity of the fitness function for individuals. This makes it easier to use the algorithm to optimize even complex systems, which is of great practical importance. Finally, the algorithm allows to solve problems consisting in choosing the structure of the solution and the parameters of this structure. The control problems considered in the simulations, where both the parameters and the structure of the PID-based controller have to be selected, are exactly this type of problem. The results obtained for the proposed algorithm are significantly better than the results obtained with the use of other methods.

Development of a volumetric compression restrainer for structures subjected to vibration

In the last 2 decades, application of viscous dampers as restrainer device (lock-up) in buildings and bridges to diminish harmful effect of vibrations. However, leakage issue in the viscous luck-up devices requires regular expensive maintenance procedure to prevent device performance failure and many researchers are encouraged to find an alternative system with low maintenance cost. Therefore, in this study the Volumetric Compression Restrainer (VCR) device has been developed as a new effective restrainer system using hyper-elastic rubber material. The VCR device is consisted of multi hyperelastic rubber tubes which positioned within steel cylinder with core and applicable in the structures as vibration resistance system. To develop VCR device, the primary design details are derived and the behavior of proposed VCR subjected to various dynamic load conditions has been assessed through the finite element simulation. In order to define the hyper-elastic characteristics of the rubber material for finite element modeling, the hyperelastic rubber material with 3 different hardness properties have been tested experimentally. Then in the next step, the rubber test data were used to develop the analytical model for the VCR device. So, the performance of the VCR device was determined by applying cyclic load to the developed FEM model. Afterwards, multiple prototypes of VCR devices have been manufactured with four different rubber hardness properties and two different configurations and a series of experimental tests have been conducted to validate the device performance.Then, a three-story concrete frame equipped with supplementary VCR device subjected to seismic loads is considered and the performance of VCR and its efficiency to diminish vibration effect are evaluated. Besides, the hybrid population-based algorithm (PSOGSA) is implemented to optimize design of structure equipped with VCR device under applied earthquake excitation. The results revealed that by implementing VCR device as supplementary restrainer system in the frame structure, lateral displacement is reduced in range of 31.5–61.2% compared to the bare structures which is proving the efficiency of developed VCR device to protect the structures against severer dynamic loads.

A comparative study of meta-heuristics for local path planning of a mobile robot

ABSTRACT Recent trends in path planning have led to a proliferation of studies that find solutions to the path planning problems in an unknown cluster environment. This study aims to find an optimum impact-free path length for a mobile robot with a multi-objective optimization approach. The multi-objective optimization problem is formulated by using path length and a safety aspect as the two objectives. A hybrid population-based optimization algorithm, i.e. the hybrid particle swarm and chemical reaction optimization (HPCRO) algorithm, has been used to obtain a smooth path for the robot in an unknown environment with circular and/or polygonal obstacles. The results of the HPCRO algorithm are then compared with those of genetic algorithms, chemical reaction optimization and particle swarm optimization. Some statistical tests are performed to illustrate the superiority and potential applicability of the hybrid algorithm. The results of the hybrid algorithm are encouraging in terms of cost function value and computational cost.

Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a Hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.

Hybrid PSOGSA technique for solving dynamic economic emission dispatch problem

In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

An Improved Hybrid Method Combining Gravitational Search Algorithm With Dynamic Multi Swarm Particle Swarm Optimization

GSA is badly suffering from a slow convergence rate and poor local search ability when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting a new neighborhood strategy. Then, by adopting the global search ability of the proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared with the other gravitational and PSO variants in terms of fitness rate.

Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems

In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GWO search for a number of iterations, and then the best-obtained solution is passed to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard GWO algorithm. We test MDGWO algorithm on seven integer programming problems and 10 minimax problems. Moreover, we compare against 11 algorithms for solving integer programming problems and 10 algorithms for solving minimax problems. Furthermore, we show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time by giving several results of the experiments.

Optimal design of vertical silicon nanowires solar cell using hybrid optimization algorithm

An approach to enhance the ultimate efficiency of the silicon nanowires (Si NWs) solar cell is proposed based on a hybrid population-based algorithm. The suggested technique integrates the ability of exploration in a gravitational search algorithm (GSA) with the exploitation capability of particle swarm optimization (PSO) to synthesize both algorithms’ strengths. The hybrid GSA-PSO algorithm in MATLAB® code is linked to finite-difference time-domain solution technique based on Lumerical-software to simulate and optimize the Si NWs’ geometrical parameters. The suggested GSA-PSO algorithm has advantages in terms of better convergence and final fitness values than that of the PSO algorithm. Further, the Si NWs lattice with optimized diameters and heights shows a high ultimate efficiency of 42.5% with an improvement of 42.8% over the Si NWs lattice with the same diameters and heights. This enhancement is attributed to the different generated optical modes combined with multiple scattering and reduced reflection due to the different heights and different diameters, respectively.

Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach

With the growing wind penetration of wind resources into power generation worldwide, accurate and comprehensive wind speed forecasting (WSF) is becoming increasingly significant to ensure continuously economical and reliable power system operations. In this paper, a novel WSF framework based on composite quantile regression outlier-robust extreme learning machine (CQR-ORELM) with feature selection and parameter optimization using a hybrid population-based algorithm is developed. The CQR-ORELM model offers flexibility and efficiency to explore potential nonlinear characteristic among wind speed variables and improve model robustness and predictive capability. A hybrid algorithm with the combination of particle swarm optimization and gravitational search algorithm (PSOGSA) is utilized to fine-tune the optimal value of weights and bias in the ORELM network structure, while the binary version of PSOGSA (BPSOGSA) is exploited asa feature selection method to construct the most relevant input feature matrix for the model. Aimed to alleviate the influence of uncertainty in wind speed time series, a time adaptive filter based empirical mode decomposition (TVF-EMD) approach is used to decompose the original wind speed series into several intrinsic mode functions (IMFs). Each IMF is well-developed using the proposed method and the final forecasting results are obtained through aggregate calculation. This proposed wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed forecast strategy to predict wind speed is evaluated by the trials of 10min ahead wind speed forecasting at two locations of the National Renewable Energy Laboratory (NREL). Comparative results confirm that the developed CQR-ORELM with feature selection procedure can describe the complete conditional distribution information hidden in variables and provide satisfactory wind speed information.

Multi‐distributed generation planning using hybrid particle swarm optimisation‐ gravitational search algorithm including voltage rise issue

Distributed generation (DG) has been becoming more well-known in the power sector because of its ability in power loss reduction, low investment cost, increase reliability, and most significantly, to exploit renewable-energy resources. In this study, a multi-objective index-based approach for optimally determining the placement and size of multi-DG units in distribution systems, including the voltage rise phenomenon is proposed. The proposed approach considers a wide range of technical aspects such as the total real power losses in the system, the voltage profile, the mega volt ampere intake by the grid, the DG quantity and the greenhouse gases emission. A novel hybrid population-based algorithm with the combination of particle swarm optimisation (PSO), and gravitational search algorithm (GSA) is introduced. To reveal the validity of the hybrid PSO-GSA, an analysis is carried out on 69-bus systems then compared with results obtained by pre-combined methods. The outcomes verify that the proposed algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimisation problem.