A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Abstract

In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems

“…Narayanan et al [58] proposed a new Many-objective SCA (MaOSCA), which uses reference points and information feedback mechanisms, and experimental results show that the MaOSCA can obtain effective and robust performance.…”

Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods

“…The following is a design for determining the location of DG. This design is determined based on the total power losses in the system according to Equations ( 29) and (30).…”

“…The minimizing of active power losses in the network is taken into consideration as the objective function in the optimization problem of optimal DG unit placement, finding the ideal placement and size of the dispersed generating units utilizing the hybrid genetic dragonfly algorithm as an optimization tool [24][25][26][27]. To allocate multiple DG units in a distribution network in the most effective way, a powerful optimization technique based on the sine cosine algorithm (SCA) and chaos map theory was proposed [28][29][30][31]. The genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), harmony search (HS), gray wolf optimization algorithm, and backtracking search optimization algorithm have all been mentioned in this paper as being used to determine the ideal size and location of DG units [32][33][34][35][36].…”

Enhancing Smart Agriculture Monitoring via Connectivity Management Scheme and Dynamic Clustering Strategy

“…Thus, hotspot failure is a significant challenge for sensor networks since it disconnects many nodes from supernodes [14][15][16]. Numerous studies have analyzed supernode mobility [14,15,17,18] and clustering [19][20][21][22][23][24][25] to overcome this challenge. The energy consumption of relay nodes can be balanced through supernode mobility and the periodic change of relay nodes.…”