International Journal of Applied Metaheuristic Computing

Genetic algorithm (GA) and gravitational search algorithm (GSA) both have successfully been applied in solving ELD problems of electrical power generation systems. Each of these algorithms has their limitations and advantage. GA's global search and GSA's local search capability are their strong points while long execution period of GA and premature of convergence of GSA hinders the possibility of optimum result when applied separately in ELD problems. To mitigate these limitations, experiment is done for the first time by combining GA and GSA suitably and applying the hybrid in non-linear ELD problems of 6, 15, and 40 unit test systems. The paper reports the details of this study including comparative analysis considering similar hybrid algorithms. The result strongly attests the quality, consistency, and overall effectiveness of the GA-GSA hybrid in ELD problems.

D. Santra

A. Mukherjee

K. Sarker

S. Mondal

Hybrid Genetic Algorithm-Gravitational Search Algorithm to Optimize Multi-Scale Load Dispatch

混合遗传算法-引力搜索算法优化多尺度负荷调度

Energy optimization and coverage area optimization of wireless sensor networks (WSN) are two major challenges to accomplish reliability optimization in the field of WSN. Reliability optimization in the field of WSN is directly connected to the performance and efficiency and consistency of the network. In this paper, the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. They have configured an optimized route/path using ant colony optimization (ACO) algorithm and deployed static WSN nodes. After configuring an efficient network, if we can maximize the coverage area, then we can ensure that the network is a reliable network. For coverage area optimization, they used a hybrid differential evolution-quantum behaved particle swarm optimization (DE-QPSO) algorithm. The result has been compared with existing literature, and the authors found good results applying those meta-heuristic and hybrid algorithms.

Avishek Banerjee

Victor Das

Arindam Biswas

Samiran Chattopadhyay

Utpal Biswas

Development of Energy Efficient and Optimized Coverage Area Network Configuration to Achieve Reliable WSN Network Using Meta-Heuristic Approaches

基于元启发式方法的能量高效优化覆盖区域网络配置以实现可靠无线传感器网络的发展

Internet of things (IoT) aims to shift intelligence to things and tends to increase the spectrum utilization efficiency. However, in doing so, it might generate high interference to the primary users (PUs) due to massive data flow into the networks. Cognitive radio smartly addresses this challenge by enabling different spectrum sharing modes while guaranteeing the quality of service. Motivated by this fact, the incorporation of cognitive abilities in IoT has given birth to a new sub-domain in IoT, known as Cognitive IoT (CIoT). This paper considers a single cell scenario in which multiple CIoT users (CUs) coexist with a PU in an underlay environment, and their communication performance has been optimized while adhering to the transmit power and interference constraints. Furthermore, two swarm intelligence-based implementations of the proposed algorithm have been provided, one based on Artificial Bee Colony (ABC) and the other based on Particle Swarm Optimization (PSO), and their effectiveness to solve the constrained power allocation problem for CIoT networks has been proved through simulations.

Babar Sultan

Imran Shafi

Jamil Ahmad

Swarm Intelligence-Based Uplink Power Control in Cognitive Internet of Things (CIoT) for Underlay Environment

基于群智能的认知物联网（CIoT）在上行链路功率控制中的应用

This work presents an ensemble method which combines both the strengths and weakness of particle swarm optimization (PSO) with genetic algorithm (GA) operators like crossover and mutation to solve the vehicle routing problem. Given that particle swarm optimization and genetic algorithm are both population-based heuristic search evolutionary methods as used in many fields, the standard particle swarm optimization stagnates particles more quickly and converges prematurely to suboptimal solutions which are not guaranteed to be local optimum. Although both PSO and GA are approximation methods to an optimization problem, these algorithms have their limitations and benefits. In this study, modifications are made to the original algorithmic structure of PSO by updating it with some selected GA operators to implement a hybrid algorithm. A computational comparison and analysis of the results from the non-hybrid algorithm and the proposed hybrid algorithm on a MATLAB simulation environment tool show that the hybrid algorithm performs quite well as opposed to using only GA or PSO.

Justice Kojo Kangah

Justice Kwame Appati

Kwaku F. Darkwah

Michael Agbo Tettey Soli

Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem

车辆路径问题的H-PSOGA优化模型实现

The joint trajectory of the robot needs to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the trajectory path. This can be achieved by limiting the travel time, kinematic and dynamic variations of the robot joints like the jerks, and torque induced in the joints in the travel of the robot. As the objectives of total travel time and joint jerk and torque rate are contradictory functions, non-dominated sorting genetic algorithm-II (NSGA-II) approach has been used to obtain the pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the pareto front with best trade-off between objectives for further optimal trajectory generation. From the simulation results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of Kawasaki RS06L industrial manipulator with minimal jerk, torque rate, and total travel time for smooth travel of robot with higher positional accuracy.

Amruta Rout

None Deepak BBVL

Bibhtui Bhusan Biswal

Golak B. Mahanta

Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

工业机器人受限运动学和动力学变量轨迹生成以提高定位精度

Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, maps, stories, dialogues, quests, and characters. A wide variety of search algorithms can be applied to PCG problems; however, those most often used are variations of evolutionary algorithms. This study focuses on comparing three metaheuristic approaches applied to racetrack games, with the specific goal of evaluating the effectiveness of different algorithms in producing game content. To that end, a Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are applied to a game-level design task to attempt to identify any discernible differences in their performance and identify whether alternative algorithms offer desirable performance characteristics. The results of the study indicate that both the ABC and PSO approaches offer potential advantages to Genetic Algorithm implementation.

Sana Alyaseri

Andy Conner

Comparative Analysis of Metaheuristic Algorithms for Procedural Race Track Generation in Games

游戏过程中赛道生成的元启发式算法比较分析

The Multidimensional Knapsack Problem (MDKP) stands as a prominent challenge in combinatorial optimization, with diverse applications across various domains. The Artificial Bee Colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of bees. The aim of this paper is to develop an ABC with the goal of improving the solution quality in comparison to previous studies for the MDKP. In the proposed ABC algorithm, a heuristic method is presented to make employed bees. The roulette wheel and k-tournament methods are investigated for selecting employed bees by onlooker bees. For crossing over, two methods including one-point and uniform are studied. To tune the parameters, the Design of Experiment (DOE) method has been applied. The well-known benchmark test problems have been used to evaluate the proposed algorithm. The results show the absolute superiority of the solutions generated by the proposed algorithm in compared with the previous studies.

Niusha Yaghini

Mir Yasin Seyed Valizadeh

An Artificial Bee Colony Algorithm for the Multidimensional Knapsack Problem

多维背包问题的人工蜂群算法

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

Abid Sabrina

Debbat Fatima

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

基于混合生物启发算法ACO-GA的多故障诊断

In this article, the authors propose a maximum flow algorithm based on flow matrix. The algorithm only requires the effort to reduce the capacity of the underutilized arcs to that of the respective flow. The optimality of the algorithm is proved by the max-flow min-cut theorem. The algorithm is table-based, thus avoiding augmenting path and residual network concepts. The authors used numerical examples and computational comparisons to demonstrate the efficiency of the algorithm. These examples and comparisons revealed that the proposed algorithm is capable of computing exact solutions while using few iterations as compared to some existing algorithms.

Trust Tawanda

Philimon Nyamugure

Elias Munapo

Santosh Kumar

Extreme Min – Cut Max – Flow Algorithm

极小割极大流算法

Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality.

短名
Journal Impact	0.48
国际分区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q4)

涉及主题	计算机科学数学人工智能数学优化算法机器学习工程类程序设计语言操作系统生物经济物理粒子群优化哲学元启发式地理计算机网络统计地质学
出版信息	出版商: IGI Global Publishing，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2018，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 63.92%

International Journal of Applied Metaheuristic Computing

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