Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

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A Comprehensive Review of Swarm Optimization Algorithms

Unit Commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads and generations under different generational, environmental and technical constraints. With the significant increase of Renewable Energy Sources (RES) integration into the power networks, effects posed by these system changes to the UC are actively being studied and investigated by global researchers and operation engineers. To this end, this paper firstly provides a literature survey of UC concept, objectives and constraints. Different UC models developed for addressing RES impacts are also reviewed. Moreover, many algorithms have been proposed in the past few decades to optimize the UC problem. This work explores the necessity for alternative optimization approaches for UC solution. In doing that, the work uncovers the advantages and disadvantages of the existing methodologies so that future algorithms could be designed in retaining the advantages of the existing methodologies while avoiding the presented weaknesses. In addition, installation of energy storage devices to balance the fluctuation in power generation and their associated impacts on UC models are reviewed. The contents of this paper provide ready-to-refer and ready-to-use information for the researchers working in the field of UC.

Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review

Coordinated design of PSSs and SVC via bacteria foraging optimization algorithm in a multimachine power system

A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design

An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments

For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI) : Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An elaborate comparative analysis is carried out to endow these algorithms with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.

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V. Selvi

Papers

Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques