Diminution of Real Power Loss by Hybridization of Particle Swarm Optimization with Extremal Optimization

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. The proposed approach is shown to have superior performance and great capability of preventing pre- mature convergence across it comparing favourably with the other algorithms. We demonstrated that our proposed HPSOEO (hybrid particle swarm optimization – Extremal optimization) presents a better performance when compared to the other algorithms. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that HPSOEO is more efficient than others for solution of single-objective Optimal Reactive Power Dispatch problem. Keywords: Modal analysis, optimal reactive power, Transmission loss, particle swarm, Particle swarm optimization, Extremal optimization, Numerical optimization, Metaheuristic

Adaptive fuzzy particle swarm optimization for flow-shop scheduling problem

Ovaj rad razmatra novi pristup problemu raspoređivanja u protočnoj proizvodnji korištenjem kombinacije neizrazite logike i optimizacije rojevima čestica u cilju postizanja sub-optimalnog rješenja. Predlaže se upotreba Tip-1 i Tip-2 modela neizrazite logike u kombinaciji s adaptivnim modelom rojeva čestica. Razvijeni model je uspoređen na standardiziranim testnim funkcijama za stohastičke algoritme (prvo jednokriterijske, a zatim višekriterijske postavljene funkcije cilja) kako bi se utvrdila njegova upotrebljivost na opće postavljenim problemima. Zatim je testiran na standardiziranim testnim zadacima za probleme protočne proizvodnje te konačno na dva praktična problema protočne proizvodnje (linije montaže i linije pakiranja). Rezultati ostvareni novim modelom su uspoređeni s konvencionalnim pravilima prioriteta te je pokazan kvantitativan i kvalitativan napredak primjenom hibrida neizrazite logike i rojeva čestica.This paper describes the application of a hybrid of fuzzy logic and swarm intelligence in order to achieve sub-optimal solutions for flow-shop scheduling problem. A novel adaptive approach with fuzzy particle swarm optimization is proposed. The developed model is tested with the standardized test functions and compared with selected stochastic algorithms (first with one objective functions and later with multi objective functions) to determine its applicability to general problems. Benchmark examples were utilized to evaluate the approach and determine the optimal number of the algorithm evaluations. Finally, the proposed model is applied on two practical problems of flow production problems (assembly lines and packaging lines). The results achieved were compared with the conventional priority rules and the effectiveness of the application of hybrid fuzzy logic and adaptive particle swarm optimization algorithm was demonstrated

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts