MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization

Nobahari, Hadi; Bighashdel, Ariyan

doi:10.1109/csiec.2017.7940171

Cited by 22 publications

(4 citation statements)

References 26 publications

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“…A set of novel CSA algorithms are extended for solving MOPs. A Multi-Objective Crow Search Algorithm (MOCSA) is proposed in [59], in which chaos and orthogonal opposition-based operators are used to hybridize CSA, (M2O-CSA) with a focus on solving MOPs. Also, a hybridization of the CSA algorithm with a clustering model is published and denoted by the Multi-objective Taylor Crow Optimization algorithm (MOTCO) considered for solving the clustering-aware wireless sensor network [60].…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

et al. 2022

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Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs to date. This paper proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function, which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. Two variants of the proposed DB-CSA approach are developed: the first variant is used to solve a set of MaOPs with 2, 3, 5, 7, 8, 10,15 objectives, and the second aims to solve several types of DMOPs with different time-varying Pareto optimal sets and a Pareto optimal front. The second variant of DB-CSA algorithm (DB-CSA-II) is proposed to solve DMOPs, including a dynamic optimization process to effectively detect and react to the dynamic change. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics used to compare the DB-CSA approach to the state-of-the-art MOEAs. The Taguchi method has been used to manage the meta-parameters of the DB-CSA algorithm. All quantitative results are analyzed using the non-parametric Wilcoxon signed rank test with 0.05 significance level, which validated the efficiency of the proposed method for solving 44 test beds (21 DMOPs and 23 MaOPS).

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Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

et al. 2022

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“…CSA has been extended in a way to solve MOPs as well. A Multi-Objective Crow Search Algorithm (MOCSA) is proposed in [62], for instance in which chaos and orthogonal opposition-based operators are used to hybridize CSA, (M2O-CSA) with a focus on solving MOPs. Also, the Multi-objective Taylor Crow Optimization algorithm (MOTCO) is proposed for clustering aware wireless sensor network [63].…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

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“…However, weights cannot be utilized as the criteria to distinguish the first category from the second category, since a series of multi-objective algorithms also utilize weights to determine a complete Pareto front, such as: the multiobjective bat algorithm [33], which utilizes K randomly chosen weights to solve K-objective problems; the multiobjective evolutionary algorithm based on decomposition [34], [35], which decomposes a MOP into several subproblems and solve them by aggregation, including the weighted sum approach, the Tchebycheff approach and the boundary intersection approach; the multi-objective evolutionary algorithm based on decomposition with adaptive replacement strategies [36], which utilizes a sigmoid function to adaptively adjust replaced neighbors of individuals in MOEA/D; the multi-objective evolutionary algorithm based on decomposition with composite operator selection [35], which introduces four types of cross-mutate operations for evolutionary computations; the multi-objective crow search algorithm [37], which utilizes a set of determined weight vectors and employees the max-min strategy; All of them are trying to search for a significant Pareto front while applying weights. To guarantee the robustness of different multi-objective optimization algorithms and solve the dynamic multi-objective optimization problems [38], prediction models such as moving average, autoregressive and single exponential smoothing can also be aggregate with weights to achieve this goal.…”

Section: Related Workmentioning

confidence: 99%

A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

et al. 2020

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Multi-objective optimization problems (MOPs) have received much attention in recent years. To deal with these problems, many multi-objective optimization algorithms have been proposed, especially the heuristic algorithms. In this paper, we proposed a multi-objective optimization algorithm called vascular invasive tumor growth optimization (VITGO), which based on the invasive tumor growth optimization and utilized a vascular mechanism to solve the MOPs. The newly proposed algorithm contains two parts: the vascular units and tumor cells. The former ones are utilized to record the Pareto solutions of the MOPs and define the search direction, and the latter ones are utilized to cooperate with vascular units to search deeper and wider. The mechanisms in the VITGO algorithm includes: endpoint generation, approximate Pareto front guidance, opposite searching, and adaptively detailed searching. Experiments showed that compared with other state-of-the-art multi-objective optimization algorithms, VITGO performs better in convergence and diversity.

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MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization

Cited by 22 publications

References 26 publications

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

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