International Journal of Applied Metaheuristic Computing

When it comes to making financial decisions, choosing stocks is crucial to building a successful portfolio. Stocks are evaluated according to lower risk, and the best stocks are chosen to produce assets that are then utilized to construct the portfolio. In this study, we have compared the integrated particle swarm method to four other algorithms for stock selection and optimization: the genetic algorithm, the Pareto search algorithm, the pattern search algorithm, and quadratic programming in the Matlab toolbox. Six particular stock firms are taken into consideration for this reason during a given time period. First, we will use the aforementioned Matlab toolbox techniques to conduct Markowitz's mean variance model. Additionally, the usual embedded particle swarm methodology and the penalty function approach will be used to create this model. The difference between the averages at ten distinct levels of predicted values will be examined in the next research based on the returns in the chosen portfolios. Statistical tests will be employed to differentiate noteworthy distinctions between the suggested approach and the alternative algorithms. MSC: 65K10,91B05.

Samaneh Mohammadi Jarchelou

Kianoush Fathi Vajargah

Parvin Azhdari

Optimizing Stock Portfolio Using the Particle Swarm Optimization Algorithm and Assessing PSO and Other Algorithms

使用粒子群优化算法优化股票投资组合并评估PSO与其他算法

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

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.

Abbreviation
Journal Impact	0.48
Quartiles(Global)	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q4)

Publication Information	Publisher: IGI Global Publishing，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2018，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 63.92%

International Journal of Applied Metaheuristic Computing

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Optimizing Stock Portfolio Using the Particle Swarm Optimization Algorithm and Assessing PSO and Other Algorithms

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

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

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

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

Optimizing Stock Portfolio Using the Particle Swarm Optimization Algorithm and Assessing PSO and Other Algorithms

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

An Artificial Bee Colony Algorithm for the Multidimensional Knapsack Problem

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

Extreme Min – Cut Max – Flow Algorithm

ISSN	1947-8283, 1947-8291
h-index	8