The ability to mutually interact is a fundamental social behavior in all human and insect societies. Social interactions enable individuals adapt improve faster than biological evolution based on genetic inheritance alone. This the driving concept behind optimization algorithm introduced this paper that makes use of intra intersociety within formal society civilization model solve single objective constrained problems. A corresponds cluster points parametric space while set such Every has...

This paper presents a new optimization algorithm to solve multiobjective design problems based on behavioral concepts similar that of real swarm. The individuals swarm update their flying direction through communication with neighboring leaders an aim collectively attain common goal. success the is attributed three fundamental processes: identification set leaders, selection leader for information acquisition, and finally meaningful transfer scheme. proposed mimics above processes employs...

Decomposition-based evolutionary algorithms have been quite successful in solving optimization problems involving two and three objectives. Recently, there some attempts to exploit the strengths of decomposition-based approaches deal with many objective problems. Performance such are largely dependent on key factors: 1) means reference point generation; 2) schemes simultaneously convergence diversity; 3) methods associate solutions directions. In this paper, we introduce a algorithm wherein...

Over the last few decades, a number of differential evolution (DE) algorithms have been proposed with excellent performance on mathematical benchmarks. However, like any other optimization algorithm, success DE is highly dependent search operators and control parameters that are often decided priori. The selection parameter values itself combinatorial problem. Although considerable investigations conducted regards to selection, it known be tedious task. In this paper, algorithm uses new...

Many-objective optimization refers to the problems containing large number of objectives, typically more than four. Non-dominance is an inadequate strategy for convergence Pareto front such problems, as almost all solutions in population become non-dominated, resulting loss pressure. However, some it may be possible generate using only a few rendering rest objectives redundant. Such reducible manageable relevant which can optimized conventional multiobjective evolutionary algorithms (MOEAs)....

Abstract In this paper a new swarm algorithm for single objective design optimization problems is presented. A collection of individuals having common goal to reach the best value (minimum or maximum) function. Among in swarm, there are some better performers (leaders) who set direction search rest individuals. An individual that not performer list (BPL) improves its performance by deriving information from closest neighbour BPL. an unconstrained problem, values used generate BPL while...

Abstract This paper presents an evolutionary algorithm for generic multiobjective design optimization problems. The is based on nondominance of solutions in the objective and constraint space uses effective mating strategies to improve that are weak either. Since methodology nondominance, scaling aggregation affecting conventional penalty function methods handling does not arise. incorporates intelligent partner selection cooperative mating. diversification strategy niching which results a...

This paper proposes a method for solving single objective constrained optimization problems by way of socio-behavioural simulation model. The essence the methodology is derived from concept that behaviour an individual changes and improves due to social interaction with society leaders. Leaders are identified after all individuals Pareto ranked according constraint satisfaction. At higher end, leaders societies interact among themselves overall improvement societies. Such leads better...

The dynamic economic dispatch problem is a high-dimensional complex constrained optimization that determines the optimal generation from number of generating units by minimizing fuel cost. Over last few decades, solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, performance algorithms highly dependent on factors, such as control parameters, diversity population, and constraint-handling procedure used. In paper, self-adaptive...

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to be optimized. A number of efficient decomposition-based evolutionary algorithms have been developed in the recent years solve them. However, computationally expensive MaOPs scarcely investigated. Typically, surrogate-assisted methods used literature tackle problems, but such studies largely focused on with 1-3 objectives. In this paper, we present an approach called hybrid many-objective algorithm...

Multiobjective optimization problems with more than three objectives are commonly referred to as many-objective (MaOPs). Development of algorithms solve MaOPs has garnered significant research attention in recent years. "Decomposition" is a adopted approach toward this aim, wherein the problem divided into set simpler subproblems guided by reference vectors. The vectors often predefined and distributed uniformly objective space. Use such uniform distribution shown commendable performance on...

A cooperative coevolutionary algorithm (CCEA) is an extension to evolutionary (EA); it employs a divide and conquer strategy solve optimization problem. In its basic form, CCEA splits the variables of problem into multiple smaller subsets evolves them independently in different subpopulations. The dynamics far more complex than EA performance can vary from good bad depending on separability This paper provides some insights why form not suitable for nonseparable problems introduces with...

This paper proposes an efficient, adaptive constraint handling approach that can be used within the class of evolutionary multi-objective optimization (EMO) algorithms. The proposed is presented framework one most successful algorithms i.e. algorithm based on decomposition (MOEA/D) [1]. mechanism adaptively decides violation threshold for comparison. type constraints, size feasible space and search outcome. Such a process intrinsically treats objective function values separately adds...

A number of real-world problems involve extremization multiple conflicting objectives, referred to as multiobjective optimization problems. Multiobjective evolutionary algorithms (MOEAs) have been widely adopted obtain Pareto front (PF) approximation for such An indispensable step in development and evaluation MOEAs is benchmarking, which involves comparisons with peer using performance metrics, hypervolume (HV) inverted generational distance (IGD). However, the de-facto practice use final...

Bilevel optimization refers to a hierarchical problem in which needs be performed at two nested levels, namely the upper level and lower level. The aim is identify optimum of problem, subject optimality corresponding problem. Several problems from domain engineering, logistics, economics, transportation have inherent structure requires them modeled as bilevel problems. usually inordinate amount function evaluations since search conducted for evaluating each solution. are especially high when...

Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this paper focuses on problems where emanate variables. In commercial designs, being is often more practical value than globally best (but unreliable). poses three key challenges: 1) appropriate formulation the problem ; 2)...