A Discrete Harmony Search Algorithm

Dai

Mathematical Problems in Engineering

et al. 2017

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

Section: Methodsmentioning

confidence: 99%

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Dai

Mathematical Problems in Engineering

et al. 2017

J. Mod. Power Syst. Clean Energy

“…The HS algorithm is a new metaheuristic optimization method and has been used to tackle various optimization problems in discrete and continuous space successfully [22]. The capability of solving complex and discrete optimization problems was demonstrated by finding the most proper solution in TSP and WDS in comparison with GA and other analytical optimization methods [20,21,23].…”

Section: Hs Algorithmmentioning

confidence: 99%

Simultaneous optimization of electrical interconnection configuration and cable sizing in offshore wind farms

Sedighi

Moradzadeh

Kükrer

et al. 2018

Offshore wind farm (OWF) is the largest renewable energy resource. The electrical interconnection cost of OWFs is a considerable fraction of the overall design cost of the farm. In order to minimize the investment and operational costs, this paper proposes an optimization formulation to find the optimal electrical interconnection configuration of wind turbines (WTs), and the optimal cable sizing simultaneously. This simultaneous minimization of total trenching length and cable dimensions creates a complex optimization problem that is solved by the harmony search (HS) algorithm. In this paper, two distinct methods of full and partial optimal cable sizing are considered to comprehensively assess the optimal interconnection layout of OWFs. Furthermore, various shipping and burying costs as well as various WTs power ratings are considered in order to investigate their impact on the optimal electrical interconnection system. The optimal electrical interconnection design obtained by the HS algorithm corresponds to a lower cost that together with the technological developments can help policy makers increase the use of offshore wind energy as a feasible unlimited renewable resource in their energy production portfolios.

“…are calculated. On the other hand, the performance of the proposed IBBA-RSS algorithm is compared with six different algorithms that are reported in [37]: NGHS1 [36], SBHS [37], BHS [38], DBHS [39], ABHS [40] and ABHS1 [41]. Table 2 shows the comparisons between the proposed algorithm and six algorithms, where best results are highlighted in bold.…”

Section: Low-dimensional 0-1 Knapsack Problemsmentioning

confidence: 99%

New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.