Gravitational search algorithm (GSA) is one of the newest swarm based optimization algorithms, which has been inspired by the Newtonian laws of gravity and motion. GSA has empirically shown to be an efficient and robust stochastic search algorithm. Since introducing GSA a convergence analysis of this algorithm has not yet been developed. This paper introduces the first attempt to a formal conve...

Slightly expanded version of a paper submitted to AISB-95. Abstract Several local search algorithms for propositional satissability have recently been proposed which are able to solve hard random problems beyond the range of conventional backtracking procedures. In this paper, we explore the impact of focusing search in these procedures on the \unsatissed variables"; that is, those variables wi...

Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result in very large scale combinatorial optimization problems. In this paper, we report on the solution of some of the largest stochastic combinatorial optimization problem consisting of ...

The use of commercial flowsheeting programs enables straight-forward rigorous, but user hidden, mathematical formulations chemical processes. optimization such black-box models is a challenging task due to nonconvexity, absence accurate derivatives, and simulation convergence failures which can prevent classical procedures from continuing the search. Here, we present an framework based on exten...

This work investigates and develops a backtracking algorithm with a novel approach to enumerating and traversing search space. A basic logical framework inspired by relevant logics is presented, highlighting relationships between search and refutation proof construction. Mechanisation of a relevance aware Davis Putnam Logemann Loveland procedure is investigated, and this yields an intelligent b...

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions solved backtracking tree search augmented logical inference. this paper, we show how quantum algorithms can accelerate at both the levels of inference search. Leveraging existing algorithms,...

In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Arti cial Intelligence can be formalized as distributed CSPs. We prese...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...

In this article, a very recently swarm optimization technique namely a backtracking search optimization algorithm (BSOA) is addressed to assign the distributed generators (DGs) along radial distribution networks. One of the main features of the BSOA is a single control parameter and not over sensitive to the initial value of this factor. The objective function is adapted with weighting factor t...

Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling means assigning a value to a variable.) Solu...