Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design

Abstract

Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.

Dynamic Street Parking Space Using Memetic Algorithm for Optimization

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Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

“…Sedlaczek and Eberhard [39] proposed an augmented Lagrangian PSO algorithm which combined the conventional PSO with the augmented Lagrangian multiplier. According to the optimization problem presented in our research work, a multilevel penalty function [40,41] based method was adopted to transform the constraint problem into an unconstraint problem. A modified objective function F is applied, which turns the inequality constraint problem as follows:…”

“…Then, the effectiveness of the algorithms before and that after improvement were compared in the experiment. According to Garg [41], GA is good at reaching a global region, but the weakness is that if an individual is not selected then the information contained by that individual is lost. PSO is good at searching for an optimal solution with the help of group interactions, but without a selection operator, PSO may waste resources on poor individuals.…”

Emergent computing and its applications

“…Syed et al [10] presents a Persistence-Extreme Learning Machine (P-ELM) algorithm to forecast the solar irradiance over time with high precision. The sixth paper 'Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design' by O. Kulkarni et al [11] proposes two constraint handling mechanisms (static penalty function and dynamic penalty function) for an emerging metaheuristic -Cohort Intelligence (CI) and illustrates the efficiency of the mechanisms on 20 well-known benchmark problems. The seventh paper 'Cohort intelligence algorithm for discrete and mixed variable engineering problems' by I.R.…”