A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

Project scheduling models with resource constraints and multi-mode activities aims to create a schedule for carrying out activities considering precedence constraints and available resources in order to minimize the project duration. In the real world, we face uncertainty related to projects, where there are no historical data, hence, we should rely on the experts' judgements to estimate activity durations. For this purpose, in this paper, the 99-simulation method is used to deal with uncertainty. The exact mathematical programming model is presented in this paper and the hybrid algorithm based on Genetic Algorithm is used to solve this type of project scheduling problem which finds the near-optimal solution in a short computational time. Finally, the effectiveness of the proposed model is examined with a numerical example.Peer reviewedFinal article published.MRCPSPSimulationUncertaintyGenetic algorith

Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

 Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 201

A Survey of League Championship Algorithm: Prospects and Challenges

The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.Comment: 10 pages, 2 figures, 2 tables, Indian Journal of Science and Technology, 201

Optimized task scheduling based on hybrid symbiotic organisms search algorithms for cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Task scheduling algorithms are responsible for specifying adequate set of resources to execute user applications in the form of tasks, and schedule decisions of task scheduling algorithms are based on QoS requirements defined by the user. Task scheduling problem is an NP-Complete problem, due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms incur high computational complexity and cannot effectively obtain global optimum solutions. Recently, Symbiotic Organisms Search (SOS) has been applied to various optimization problems and results obtained were found to be competitive with state-of-the-art metaheuristic algorithms. However, similar to the case other metaheuristic optimization algorithms, the efficiency of SOS algorithm deteriorates as the size of the search space increases. Moreover, SOS suffers from local optima entrapment and its static control parameters cannot maintain a balance between local and global search. In this study, Cooperative Coevolutionary Constrained Multiobjective Symbiotic Organisms Search (CC-CMSOS), Cooperative Coevolutionary Constrained Multi-objective Memetic Symbiotic Organisms Search (CC-CMMSOS), and Cooperative Coevolutionary Constrained Multi-objective Adaptive Benefit Factor Symbiotic Organisms Search (CC-CMABFSOS) algorithms are proposed to solve constrained multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. To address the issue of scalability, the concept of Cooperative Coevolutionary for enhancing SOS named CC-CMSOS make SOS more efficient for solving large scale task scheduling problems. CC-CMMSOS algorithm further improves the performance of SOS algorithm by hybridizing with Simulated Annealing (SA) to avoid entrapment in local optima for global convergence. Finally, CC-CMABFSOS algorithm adaptively turn SOS control parameters to balance the local and global search procedure for faster convergence speed. The performance of the proposed CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are evaluated on CloudSim simulator, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are compared with multi-objective optimization algorithms, namely, EMS-C, ECMSMOO, and BOGA. The CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) while meeting deadline constraints with no computational overhead. The performance improvements obtained by the proposed algorithms in terms of hypervolume ranges from 8.72% to 37.95% across the workloads. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ1 and λ2 were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers