Swarm Magnetic Optimizer: A New Optimizer that Adopts Magnetic Behaviour

Constructing a multi-day travel itinerary is a notable challenge, particularly for individuals planning extended trips. This study addresses the complexity by aiming to automatically generate an optimal multi-day travel itinerary that satisfies user interests. The problem is framed as a Capacitated Vehicle Routing Problem with Time Window (CVRPTW), and user interests are shaped by attributes such as rating and cost of points of interest (POIs), travel duration, the number of POIs in the itinerary, and penalty attributes (POI penalty and time penalty). The generated solution must adhere to constraints like daily travel duration limits and the operational hours of POIs. To ensure alignment with user interests, the Multi-Utility Attribute Theory (MAUT) is employed as the fitness function. This study proposes a VRP approach utilizing the hybrid Ant Colony System (ACS) and Brainstorm optimization (BSO) algorithm (the hybrid ACS-BSO) for multi-day travel itinerary generation, addressing the Traveling Salesman Problem (TSP) approach limitations. The hybrid ACS-BSO outperforms conventional algorithms, such as Genetic Algorithm (GA), Tabu Search (TS), and Simulated Annealing (SA), across 5 sets of random POIs with an average fitness value of 0.6704. moreover, the hybrid ACS-BSO outperforms the other conventional algorithms in optimizing each attribute. In terms of travel duration attribute, the hybrid ACS-BSO generate an itinerary requiring only 6 days to visit 40 POIs, while the other algorithms need 7 days. In terms of cost and rating attributes, the hybrid ACS-BSO achieves the best fitness values compared to the others. Furthermore, the hybrid ACS-BSO outperforms the standalone algorithms (ACS and BSO) across varying numbers of POIs, but it faces a maximum 311 seconds running time for 87 POIs, indicating a time complexity weakness. Comparatively, ACS, BSO, and the hybrid ACS-BSO in VRP approach surpass their TSP counterparts, affirming the effectiveness of the VRP approach.

Section: 𝐷| ≤ 𝑁mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

2024

“…Moreover, as per NLF, many researchers have been motivated to introduce new algorithms. The extended stochastic coati optimiser (ESCO) [26], swarm magnetic optimiser (SMO) [27], walk-spread algorithm (WSA) [28], four directed search algorithms (FDSA) [29], and migrating walrus algorithm (MWA) [30] are recent meta-heuristics for addressing various real-time optimisation problems. The Sparrow search algorithm (SSA) is a recent and efficient algorithm, but it suffers from slow convergence [31].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Sparrow Search Algorithm for Solving Optimal Power Flow Problem Considering Renewable Energy Systems

2024

In this paper, an enhanced sparrow search algorithm (ESSA) is proposed to solve the optimal power flow (OPF) problem of power systems considering renewable energy systems. Two modifications were introduced to the basic SSA to overcome its weak global search capability. Elite reverse learning strategy for diversifying the population and mutation strategy of firefly algorithm (FA) to achieve a fast convergence rate. The effectiveness of the ESSA is analysed for two types of power systems: conventional power systems with only thermal power plants and modern power systems with thermal and renewable energy power plants. A multi-objective optimisation problem considering the operational cost of power plants and greenhouse gas (GHG) emissions was formulated with multiple power system variables and its operational equal and unequal constraints. The effectiveness of ESSA was verified on standard IEEE 14-bus and IEEE 30-bus test systems without RE plants and compared with the literature. In addition, a real-time Andhra Pradesh, an Indian power system with 14-bus is simulated with RE systems. In the 14-bus test system, the cost is reduced by 89.97 $/h, the losses are reduced by 4.106 MW, the voltage deviation is reduced by 0.1276 p. u., and GHG emissions are reduced by 8407.86 lb/MWh. In the 30-bus test system, the cost was reduced by 73.1 $/h, the losses were reduced by 3.962 MW, the voltage deviation was reduced by 0.00459 p. u., and GHG emissions were reduced by 8113 lb/MWh. On the other hand, in a real-time system, the cost is reduced by 114.41 $/h, the losses are reduced by 1.177 MW, the voltage deviation is reduced by 0.1116 p. u., and GHG emissions are reduced by 75560.13 lb/MWh. The obtained results confirm that the proposed ESSA outperforms the basic SSA and other competitive metaheuristics, namely the grasshopper optimisation algorithm (GOA), whale optimisation algorithm (WOA), and mothflame optimisation (MFO), to solve the OPF problem. However, embedding RE plants in the OPF problem has resulted in significant reductions in operating costs and GHG emissions, which are the need of the present world with rising fossil fuel costs and increasing global warming.

“…An algorithm achieves the best efficiency for a given problem, but it does not get the same performance another problem [30,31]. Furthermore, there are many new algorithms being developed nowadays like swarm-magnetic-optimizer [32], extended-stochastic coati-optimizer [33], walk-spread-algorithm [34] and growth-optimization (GO) [29], etc. They have also demonstrated their advantages compared with many previous algorithms for classic functions.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Travelling Salesman Problem Relied on Growth Optimization

2024

This paper shows a novel method relied on growth optimization (GO) algorithm for searching the shortest tour length of the travelling salesman problem (TSP). GO is a recent algorithm relied on the idea of learning and reflecting of people in the society. To enhance performance of GO, the 2-opt local search technique is applied for adjusting the candidate solutions created by GO. The effectiveness of GO is validated on five TSP instances consisting of the 14-city, 30-city, 48-city, 52-city and 76-city. The error between the optimal tour length value obtained by GO and the best-known value for these instances is 0.0000%, 0.0000%, 0.0021%, 0.0314% and 0.0004%, respectively. Furthermore, the comparisons to the methods in the literature in term of the optimal and mean tour length values have shown that GO reaches the better values compared to other prvious methods. Thus, the proposed GO approach is a potential method for the TSP problem.