Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

The alternating direction method has been used widely in the power systems field for solving the multiarea dispatch problem. However, experience with applications has shown that the convergence rate of the alternating direction method depends significantly on the selection of the penalty parameter of the linear consistency constraint. Typically, it is difficult to obtain the optimal penalty parameter in advance. In this paper, for the purpose of solving this problem, we propose centralized and distributed self-adaptive penalty parameter strategies that allow the value of the penalty parameter to increase or decrease based on the information from each iteration. Simulation results illustrate that the proposed centralized and distributed self-adaptive methods are superior to the traditional alternating direction method in terms of robustness and convergence rate.

Section: Introductionmentioning

confidence: 99%

The self-adaptive alternating direction method for the multiarea economic dispatch problem

Ren¹,

Fei²,

Wei³

2016

“…In the last few decades, EC has been studied for dealing with nondeterministic multiobjective optimization problems. The multiobjective evolutionary optimization algorithms (MOEAs) have become a very popular and fast-growing field [3][4][5]. Since Schaffer's pioneering work in 1985 [6], various MOEAs have been proposed in this field to solve several domains of science and engineering problems.…”

Section: Introductionmentioning

confidence: 99%

A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)

Sağ¹,

Çunkaş²

2016

This paper presents a new metaheuristic algorithm based on the artificial bee colony (ABC) algorithm for multiobjective optimization problems. The proposed hybrid algorithm, an improved bee colony algorithm for multiobjective optimization called IBMO, combines the main ideas of the simple ABC with nondominated sorting strategy corresponding to the principal framework of multiobjective optimization such as Pareto-dominance and crowding distance. A fixed-sized external archive to store the nondominated solutions and an improvement procedure to promote the convergence to true Pareto front are used. The presented approach, IBMO, is compared with four representatives of the state-of-the-art algorithms: NSGA2, SPEA2, OMOPSO, and AbYSS. IBMO and the selected algorithms from specialized literature are applied to several multiobjective benchmark functions by considering the number of function evaluations. Then four quality indicators are employed for performance evaluations: general distance, spread, maximum spread, and hypervolume. The results show that the IBMO is superior to the other methods.

“…The OHS algorithm employs the technique of opposition-based learning methodology for the initialization of harmony memory and for the process of jumping generation. Based on the Pareto optimal set, fuzzified ABC [24] was formulated to determine the best compromising solution for CE-ED problems. The gravitational search algorithm (GSA) [25] was formulated based on the physical laws of gravity and laws of motion, where two different objective functions are combined into a single objective function.…”

Section: Related Workmentioning

confidence: 99%

Power search algorithm (PSA) for combined economic-emission dispatch problems considering valve point effects in economic load dispatch

Arumugam¹,

Subramanian²

2017

Economic load dispatch has become the prime and major concern in power system networks. Use of nonrenewable fuels as an input to power systems results in toxic gas emission and depletion of resources for future generations. Allocation of optimal power to generators serves as a solution to this issue. The suggested power search algorithm (PSA) is a novel solution for the reduction of the fuel cost, emissions, and power loss. This algorithm searches its solution inside the search space from minimum to maximum in case of forward search. The global optimum solution is obtained with much less execution time compared to different optimization algorithms. The robustness of the PSA is tested with various IEEE test systems. The proposed PSA proves to be better than the existing algorithms.