Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems

Kulkarni, Anand J.; Baki, Mohammed Fazle; Chaouch, Ben A.

doi:10.1016/j.ejor.2015.10.008

Cited by 66 publications

(10 citation statements)

References 20 publications

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“…-In CI algorithm, at the end of every learning attempt every candidate independently updates its search space. -The problem with large number of variables and constraints can be efficiently handled [20,26].…”

Section: Cohort Intelligence Algorithmmentioning

confidence: 99%

“…The probability-based constraint handling techniques was used to handle the constraints. Similar approach was also applied for solving real world combinatorial problems from healthcare and logistics domains and large sized complex problems from the Cross-Border Supply Chain domain [20], Traveling Salesman Problem (TSP) [21] and several benchmark problems [18]. A self-adaptive Cohort Intelligence (SACI) algorithm [22] was proposed using tournament mutation operator and a self-adaptive scheme to update the sampling interval.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

Kale

Pachpande

Naikwadi

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

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The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

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Section: Cohort Intelligence Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

Kale

Pachpande

Naikwadi

et al. 2022

Int. J. Simul. Multidisci. Des. Optim.

View full text Add to dashboard Cite

show abstract

“…Step 8 (Convergence): The algorithm is assumed to have converged if all of the conditions listed in Equation (13) are satisfied for successive considerable number of learning attempts and accept any of the current behaviors as final solution * from within cohorts.…”

Section: Multi-cohort Intelligence (Multi-ci)mentioning

confidence: 99%

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

Shastri

Kulkarni

2018

International Journal of Parallel, Emergent and Distributed Sys

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A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimization domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimization, Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-adaptive differential evolution algorithm, Comprehensive Learning Particle Swarm Optimization, Backtracking Search Optimization Algorithm and Ideology Algorithm. The Wilcoxon signed rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations are discussed as well. In addition, an illustrative example is also solved and every detail is provided.

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“…Hence, the SAPF approach was proposed, which does not require parameter tuning. CI was also applied to solve the 0-1 Knapsack problem (Kulkarni and Shabir, 2016a), the travelling salesman problem (Kulkarni et al, 2017), a healthcare and inventory management problem, a seacargo mix problem and a cross-border shipper selection (Kulkarni et al, 2016b). The constraints involved with these problems were highly nonlinear and interdependent.…”

Section: Introductionmentioning

confidence: 99%

A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems

Kale

Kulkarni

Mezura-Montes

2022

Preprint

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Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior in those problems is different as it involves large number of linear, nonlinear and non-convex type equality and inequality constraints. In this work a 57 real-world constrained optimization problems test suite is solved using two constrained metaheuristic algorithms originated from a socio-based Cohort Intelligence (CI) algorithm. The first CI-based algorithm incorporates a self-adaptive penalty function approach i.e., CI-SAPF. The second algorithm combines CI-SAPF with the intrinsic properties of the physics-based Colliding Bodies Optimization (CBO) referred to CI-SAPF-CBO. The results obtained from CI-SAPF and CI-SAPF-CBO are compared with other constrained optimization algorithms such as IUDE, ϵMAg-ES and iLSHADE𝜖. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.

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Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems

Cited by 66 publications

References 20 publications

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems

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