Four Directed Search Algorithm: A New Optimization Method and Its Hyper Strategy Investigation

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21,20,17,17,and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions.

Section: Introductionmentioning

confidence: 99%

Swarm Space Hopping Algorithm: A Swarm-based Stochastic Optimizer Enriched with Half Space Hopping Search

2024

“…Besides, there are also several scientists active in the development of metaheuristics. Kusuma has introduced several metaheuristics such as attack leave optimization (ALO) [27], total interaction algorithm (TIA) [28], modified social force algorithm (MSFA) [29], four-directed search algorithm (FDSA) [30], and so on. Braik was involved in the development of the chameleon swarm algorithm (CSA) [31], white shark optimizer (WSO) [32], and so on.…”

Section: Introductionmentioning

confidence: 99%

Swarm Bipolar Algorithm: A Metaheuristic Based on Polarization of Two Equal Size Sub Swarms

2024

This paper presents a new metaphor-free metaheuristic search called the swarm bipolar algorithm (SBA). SBA is developed mainly based on the non-free-lunch (NFL) doctrine, which mentions the non-existence of any general optimizer appropriate to answer all varieties of problems. The construction of SBA is based on splitting the swarm into two equal-sized swarms to diversify the searching process while performing intensification within the subswarms. There are two types of finest swarm members: the finest swarm member for the whole swarm and the finest swarm member in every sub-swarm. There are four directed searches performed in every iteration: (1) search toward the finest swarm member, (2) search toward the finest sub-swarm member, (3) search toward the middle between two finest sub-swarm members, and (4) search relative to the randomly picked swarm member from another sub-swarm. The performance of SBA is assessed through two assessments with a set of 23 functions representing the optimization problem. In the benchmark assessment, SBA is contended with five metaheuristics: northern goshawk optimization (NGO), language education optimization (LEO), coati optimization algorithm (COA), fully informed search algorithm (FISA), and total interaction algorithm (TIA). The result presents the superiority of SBA among its contenders by being better than NGO, LEO, COA, FISA, and TIA in 21,16,16,21,and 18 functions. The single search assessment is performed to evaluate each strategy involved in SBA. The result shows that the search toward the middle between the two finest sub-swarm members is the best among the four searches in SBA.

“…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.