Group Counseling Optimization (GCO) has recently been proposed in an attempt to emulate the human social behavior in solving life problems through counseling within a group. After its promising results in solving unconstrained singleobjective and multi-objective optimization problems, in this paper, GCO is extended to solve the constrained optimization problems for the first time. Also, a hybri...

We consider equilibrium constrained optimization problems, which have a general formulation that encompasses well-known models such as mathematical programs with equilibrium constraints, bilevel programs, and generalized semi-infinite programming problems. Based on the celebrated KKM lemma, we prove the existence of feasible points for the equilibrium constraints. Moreover, we analyze the topol...

This article deals with constrained multi-objective optimization problems. The main purpose of the article is to investigate relationships between constrained and unconstrained multi-objective optimization problems. Under suitable assumptions (e.g., generalized convexity assumptions) we derive a characterization of the set of (strictly, weakly) efficient solutions of a constrained multi-objecti...

We present a general method of handling constraints in genetic optimization, based on the Behavioural Memory paradigm. Instead of requiring the problem-dependent design of either repair operators (projection on the feasible region) or penalty function (weighted sum of the constraints violations and the objective function), we sample the feasible region by evolving from an initially random popul...

Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feas...

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BRO outperforms standard BBO. Second, we employ blended BRO to solve constrained optimization problems. Constraints are handled by modifying the BRO immi...

In this paper, we study the problem of minimizing the ratio of two quadratic functions subject to a quadratic constraint. First we introduce a parametric equivalent of the problem. Then a bisection and a generalized Newton-based method algorithms are presented to solve it. In order to solve the quadratically constrained quadratic minimization problem within both algorithms, a semidefinite optim...

Many engineering optimization problems can be state as function optimization with constrained, intelligence optimization algorithm can solve these problems well. Cultural Algorithms are a class of computational models derived from observing the cultural evolution process in nature, cultural algorithms in the optimization of the complex constrained functions of its superior performance. Experime...