Multi-objective Comprehensive Learning Particle Swarm Research Articles

Recuperation based multi-objective generation expansion planning for a real-world power system

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions.

Multi-Objective Based Generation Expansion Planning for Utility Power System of Tamil Nadu State Considering Recuperation of Older Power Plants

Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental emission. Hence the mathematical representations have been developed to be accurate, and to improve the understanding of the multifaceted and contradictory aspects of the GEP problem. In this study, the Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) algorithm is implemented for solving Multi-Objective GEP (MOGEP) problem. The objectives such as minimization of overall cost, decrement of the pollutant emission and enhancement of reliability have been considered by considering the constraints. The real-world MOGEP problem has been solved for seven-year (from the year 2020 to 2027) and fourteen-year (from the year 2020 to 2034) planning span for the utility power system of Tamil Nadu state, India. The problem is solved for four different cases with the consideration of retirement and recuperation of the older generating units. coal, gas, oil, nuclear, hydel, wind, Solar-photovoltaic (SPV) and biomass power plants were considered in this planning study. The results establish the competence of MOCLPSO to produce well-spread Pareto optimal non-dominated solutions of the MOGEP problem.

Decomposition-based multi-objective comprehensive learning particle swarm optimisation

This paper proposes decomposition-based comprehensive learning particle swarm optimisation (DCLPSO) for multi-objective optimisation. DCLPSO uses multiple swarms, with each swarm optimising a separate objective. Two sequential phases are conducted: independent search and then cooperative search. Important information related to extreme points of the Pareto front often can be found in the independent search phase. In the cooperative search phase, a particle randomly learns from its personal best position or an elitist on each dimension. Elitists are non-dominated solutions and are stored in an external repository shared by all the swarms. Mutation is applied to each elitist in this phase to help escaping from local Pareto fronts. Experiments conducted on various benchmark problems demonstrate that DCLPSO is competitive in terms of convergence and diversity of the resulting non-dominated solutions.

Decomposition-based multi-objective comprehensive learning particle swarm optimisation

This paper proposes decomposition-based comprehensive learning particle swarm optimisation (DCLPSO) for multi-objective optimisation. DCLPSO uses multiple swarms, with each swarm optimising a separate objective. Two sequential phases are conducted: independent search and then cooperative search. Important information related to extreme points of the Pareto front often can be found in the independent search phase. In the cooperative search phase, a particle randomly learns from its personal best position or an elitist on each dimension. Elitists are non-dominated solutions and are stored in an external repository shared by all the swarms. Mutation is applied to each elitist in this phase to help escaping from local Pareto fronts. Experiments conducted on various benchmark problems demonstrate that DCLPSO is competitive in terms of convergence and diversity of the resulting non-dominated solutions.

Case Mix Index weighted multi-objective optimization of inpatient bed allocation in general hospital

Inpatient bed, as one of the most critical resources for general hospitals, has to be effectively allocated among different departments to achieve the goal of preferable balance between patient service and resource utilization. To address this issue, studies in the past have been focusing on the development of bed allocation optimization algorithms with the objective of improved patient admission rate or higher bed occupancy rate. But in the context of hierarchical medical system promoted by the medical reform currently ongoing in China, the level of disease severity or treatment difficulty of clinical cases also has to be involved in the evaluation of the hospital performance, especially for the highly ranked national-level general hospitals. Case Mix Index (CMI), as an internationally recognized index which is highly correlated with the level of clinical complexity, is thus introduced to evaluate hospitals uniformly and facilitate straightforward comparison among them. To be aligned with the new requirement for an improved CMI, a multi-objective comprehensive learning particle swarm optimization algorithm is proposed in this paper based on an upgraded queueing model which incorporates CMI as an important weight into the traditional optimization objectives. Experimental results indicate that a higher CMI can be achieved with the new method and meanwhile both patient admission rate and bed occupancy rate with which hospital managers are still concerned will not be much influenced. The method is developed for a tertiary public hospital in Shanghai and can also be applied by hospital managers in other hospitals of the similar scale.

Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

In particle swarm optimization (PSO) each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming to find the global or local best positions in case of complex problems. To overcome this problem, we propose a new multi-objective variant of PSO called attributed multi-objective comprehensive learning particle swarm optimizer (A-MOCLPSO). In this technique, we do not use global or local best positions to modify the velocity of a particle; instead, we use the best position of a randomly selected particle from the whole population to update the velocity of each dimension. This method not only increases the speed of the algorithm but also searches in more promising areas of the search space. We perform an extensive experimentation on well-known benchmark problems such as Schaffer (SCH), Kursawa (KUR), and Zitzler–Deb–Thiele (ZDT) functions. The experiments show very convincing results when the proposed technique is compared with existing versions of PSO known as multi-objective comprehensive learning particle swarm optimizer (MOCLPSO) and multi-objective particle swarm optimization (MOPSO), as well as non-dominated sorting genetic algorithm II (NSGA-II). As a case study, we apply our proposed A-MOCLPSO algorithm on an attack tree model for the security hardening problem of a networked system in order to optimize the total security cost and the residual damage, and provide diverse solutions for the problem. The results of our experiments show that the proposed algorithm outperforms the previous solutions obtained for the security hardening problem using NSGA-II, as well as MOCLPSO for the same problem. Hence, the proposed algorithm can be considered as a strong alternative to solve multi-objective optimization problems.

Multi-objective optimization problem for the Thyristor Controlled Series Compensators placement with multiple decision-making approaches

The optimal placement of Thyristor Controlled Series Compensators (TCSCs) in transmission systems is formulated as a multi-objective optimization problem with the objectives of simultaneously maximizing transmission system loadability, minimizing transmission loss, minimizing the cost of TCSCs, and minimizing the voltage deviation at the load buses. The thermal limits of transmission lines and voltage limits of load buses are considered as security constraints. This problem is solved in two stages. In the first stage, multi-objective particle swarm optimization, multi-objective comprehensive learning particle swarm optimizer, and weighted sum method are applied to obtain the non-dominated solutions. In the second stage, two different multi-criterion decision-making tools, namely Technique for Order Preference by Similarity to Ideal Solution and Fuzzy C-Means clustering are employed to assist the decision maker in deciding the best trade-off solution. The proposed approach is successfully tested on IEEE 14 bus system and IEEE 118 bus system. Copyright © 2011 John Wiley & Sons, Ltd.

Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems

This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto-optimal front compared to two other multiobjective optimization evolutionary algorithms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 209–226, 2006.