Many real world optimization scenarios impose certain limitations, in terms of constraints and bounds, on various factors affecting the problem. In this paper we formulate several methods for bound handling of decision variables involved in solving a multi-objective optimization problem using particle swarm optimization algorithm. We further compare the performance of these methods on different...

in this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. the objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices and increasing distribution companies’ profit. the combination of firefly algorithm, parti...

In the quest to achieve home comfort with highest possible efficiency, there is an increasing interest in combined cooling, heating and power (CCHP) systems. These can be fuelled by natural gas potentially hydrogen enriched ultimately hydrogen. The optimised designs largely depend on energy dispatch algorithms that take into account aspects of economic environmental impacts as well system effic...

The problem of maximizing the benefit from a specified number of a particular product with respect to the behavior of customer choices is regarded as revenue management. This managerial technique was first adopted by the airline industries before being widely used by many others such as hotel industries. The scope of this research is mainly focused on hotel revenue management, regarding which a...

This paper presents a new multi-objective optimization based reactive power price clearing (RPPC) mechanism, considering voltage stability. Use of realistic voltage dependent load models is important in power system analysis and optimization. The influence of the same on RPPC is investigated in this paper. Investigations have also been carried out to ascertain the effectiveness of objectives su...

The present study proposes a low-energy consumption multipoint sampler carried by deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location wide areas, an optimization method structure based on least-squares support-vector machine (LSSVM) surrogate model multi-objective particle swarm (MOPSO) algorithm. First, overall core components, such as s...

Vector evaluated particle swarm optimization (VEPSO) is a multi-swarm variant of the traditional particle swarm optimization (PSO) algorithm applied to multi-objective problems (MOPs). Each subobjective is allocated a single sub-swarm and knowledge transfer strategies (KTSs) are used to pass information between swarms. The original VEPSO used a ring KTS, and while VEPSO has shown to be successf...